{
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  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T13:33:28.892596Z",
     "start_time": "2020-10-18T13:33:28.889481Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from dateutil.parser import parse\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T13:23:44.736498Z",
     "start_time": "2020-10-18T13:23:44.731114Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('/Users/ankur.kumar/Downloads/all_submitted_files.csv', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T13:32:04.817360Z",
     "start_time": "2020-10-18T13:32:04.810331Z"
    }
   },
   "outputs": [],
   "source": [
    "data['Submitted On'] = pd.to_datetime(data['Submitted On'], format='%d/%m/%Y %X')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T15:53:19.354247Z",
     "start_time": "2020-10-18T15:53:19.196230Z"
    }
   },
   "outputs": [
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       "      <td>multi_model_svc_v6</td>\n",
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       "      <th>94</th>\n",
       "      <td>multi_model_lr_v7.csv</td>\n",
       "      <td>2020-10-18 06:56:39</td>\n",
       "      <td>multi_model_lr_v7</td>\n",
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      "text/plain": [
       "                                    File        Submitted On  \\\n",
       "85               ensemble_v8_lrModel.csv 2020-10-17 16:47:48   \n",
       "86                 multi_model_lr_v1.csv 2020-10-17 21:43:40   \n",
       "87                multi_model_svc_v2.csv 2020-10-17 21:44:25   \n",
       "88                multi_model_svc_v2.csv 2020-10-17 21:44:56   \n",
       "89                 multi_model_nn_v3.csv 2020-10-18 06:00:31   \n",
       "90                multi_model_svc_v4.csv 2020-10-18 06:04:46   \n",
       "91      multi_model_equal_weights_v5.csv 2020-10-18 06:26:59   \n",
       "92  multi_model_optimized_weights_v5.csv 2020-10-18 06:29:46   \n",
       "93                multi_model_svc_v6.csv 2020-10-18 06:56:15   \n",
       "94                 multi_model_lr_v7.csv 2020-10-18 06:56:39   \n",
       "\n",
       "                             Comment  Public Score  Private Score  \n",
       "85               ensemble_v8_lrModel      0.850222       0.853767  \n",
       "86                 multi_model_lr_v1      0.851444       0.855533  \n",
       "87                multi_model_svc_v2      0.850889       0.854733  \n",
       "88                 multi_model_nn_v3      0.850889       0.854733  \n",
       "89                 multi_model_nn_v3      0.850889       0.854667  \n",
       "90                multi_model_svc_v4      0.848667       0.853367  \n",
       "91      multi_model_equal_weights_v5      0.851444       0.855867  \n",
       "92  multi_model_optimized_weights_v5      0.851222       0.855567  \n",
       "93                multi_model_svc_v6      0.851444       0.855900  \n",
       "94                 multi_model_lr_v7      0.851889       0.856133  "
      ]
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     "metadata": {},
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    "data.tail(10)"
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     "end_time": "2020-10-18T13:32:13.923495Z",
     "start_time": "2020-10-18T13:32:13.911667Z"
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    {
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       "      <th>60</th>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>multi_model_lr_v1.csv</td>\n",
       "      <td>2020-10-17 21:43:40</td>\n",
       "      <td>multi_model_lr_v1</td>\n",
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      ],
      "text/plain": [
       "                                File        Submitted On  \\\n",
       "82               ensemble_v9_svm.csv 2020-10-17 11:13:46   \n",
       "94             multi_model_lr_v7.csv 2020-10-18 06:56:39   \n",
       "61                 ensemble_v8_1.csv 2020-10-15 09:23:56   \n",
       "60                 ensemble_v8_1.csv 2020-10-15 09:17:16   \n",
       "66         level3ensemble_new_v4.csv 2020-10-15 19:42:36   \n",
       "65                 ensemble_v9_1.csv 2020-10-15 19:42:04   \n",
       "64                 ensemble_v9_1.csv 2020-10-15 19:24:19   \n",
       "93            multi_model_svc_v6.csv 2020-10-18 06:56:15   \n",
       "91  multi_model_equal_weights_v5.csv 2020-10-18 06:26:59   \n",
       "86             multi_model_lr_v1.csv 2020-10-17 21:43:40   \n",
       "\n",
       "                         Comment  Public Score  Private Score  \n",
       "82               ensemble_v9_svm      0.852111       0.854700  \n",
       "94             multi_model_lr_v7      0.851889       0.856133  \n",
       "61                 ensemble_v8_2      0.851889       0.854533  \n",
       "60                 ensemble_v8_1      0.851889       0.854533  \n",
       "66         level3ensemble_new_v4      0.851778       0.855300  \n",
       "65         level3ensemble_new_v4      0.851556       0.854733  \n",
       "64                 ensemble_v9_1      0.851556       0.854733  \n",
       "93            multi_model_svc_v6      0.851444       0.855900  \n",
       "91  multi_model_equal_weights_v5      0.851444       0.855867  \n",
       "86             multi_model_lr_v1      0.851444       0.855533  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values('Public Score',ascending = False).head(10)"
   ]
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   "cell_type": "code",
   "execution_count": 44,
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    "ExecuteTime": {
     "end_time": "2020-10-18T13:32:14.496087Z",
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       "      <th>87</th>\n",
       "      <td>multi_model_svc_v2.csv</td>\n",
       "      <td>2020-10-17 21:44:25</td>\n",
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      "text/plain": [
       "                                    File        Submitted On  \\\n",
       "94                 multi_model_lr_v7.csv 2020-10-18 06:56:39   \n",
       "93                multi_model_svc_v6.csv 2020-10-18 06:56:15   \n",
       "91      multi_model_equal_weights_v5.csv 2020-10-18 06:26:59   \n",
       "92  multi_model_optimized_weights_v5.csv 2020-10-18 06:29:46   \n",
       "86                 multi_model_lr_v1.csv 2020-10-17 21:43:40   \n",
       "66             level3ensemble_new_v4.csv 2020-10-15 19:42:36   \n",
       "68             level3ensemble_new_v6.csv 2020-10-15 19:52:43   \n",
       "56             level3ensemble_new_v1.csv 2020-10-14 19:47:30   \n",
       "88                multi_model_svc_v2.csv 2020-10-17 21:44:56   \n",
       "87                multi_model_svc_v2.csv 2020-10-17 21:44:25   \n",
       "\n",
       "                             Comment  Public Score  Private Score  \n",
       "94                 multi_model_lr_v7      0.851889       0.856133  \n",
       "93                multi_model_svc_v6      0.851444       0.855900  \n",
       "91      multi_model_equal_weights_v5      0.851444       0.855867  \n",
       "92  multi_model_optimized_weights_v5      0.851222       0.855567  \n",
       "86                 multi_model_lr_v1      0.851444       0.855533  \n",
       "66             level3ensemble_new_v4      0.851778       0.855300  \n",
       "68             level3ensemble_new_v6      0.851111       0.854867  \n",
       "56             level3ensemble_new_v1      0.850889       0.854800  \n",
       "88                 multi_model_nn_v3      0.850889       0.854733  \n",
       "87                multi_model_svc_v2      0.850889       0.854733  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values('Private Score',ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T13:34:24.503758Z",
     "start_time": "2020-10-18T13:34:24.500039Z"
    }
   },
   "outputs": [],
   "source": [
    "new_data = data[['Submitted On', 'Public Score', 'Private Score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-18T13:34:28.347176Z",
     "start_time": "2020-10-18T13:34:27.904110Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Submitted On'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m/YozxezDhq/rZ5BqyiBvLFz0srlv1fuw6kNY+oaZ7dT7BBPM9T01cuVUoSWU854DFBBobGKxmYYEmYGALqM/ZA4wjRo6y8y4SK+B81SZAK3wh0DQNs+sVQPzM8gZDmNvhO5jwD0aErNh1kOm3K0jKQXJueaj17GNPyYlD675CD7/HXz3mPkaL5hubhfNV74X4tPq3xY8MWQNCbysNjPUOm8cvHODyYKe97iZ1XYovhqwdsK1bk2xWLp08AZdMYD76G5z5i+SsgfDafeF9VCv18tHH33EqaeaSfELFixg2bJl9OxZ/wzFRRddxOuvv84ZZ5xBTU0NX3zxBY8//jhaaz777DMcDgdr167lkksuYd68edx3333cf//9fPDBBwA89dRTJCcnM3fuXKqrq5k4cSKTJ0+u9zqTJ0/m97//PX379uWkk07ioosu4thjj6WmpoaLLrqIGTNmMHr0aEpKSnA6nTz00EMopVi6dCmrVq1i8uTJrFmzpvbrWLJkCampqfzqV7/ihBNO4LnnnuPAgQOMGTOGk046ifj4NjijKoQQ7em0v5tywaJVZvbQ2k9g0b/r7o9JNIFdZn8T3GUEysOScpsf2AXPcLc2gFDKHFg17BrYnmxOEzCAuSxaZQ6+7Q5TRtnvVDjTazKOqz6AlR/A6g/N2qceE2HA2eYgMCmn5fsQmoGLSYS7VpuRArtXwe4VZp92LIEV71Eb2FljTCON4M8zM/Dh6tF4I49I84eULNa0ooRSayjeWheoFf5gvlZ/4CRBSj7kT6gL1rIGNZ4RmXiH+egKbDFw6l9MMPGfW+GJSXDek+Z3TYSvdBc8ONBk0wacWXd7aAllQ/1Ogxu+gdevgtcuhQm3wYm/a/pvmbca7M7I77toUtcL4DpIZWUlw4YNA0wG7rrrrmP27NmMGTPmoOAN4LTTTuOOO+6gurqajz/+mGOOOQan00lxcTG33norixYtwmq11gZQDX366acsWbKEN998E4Di4mLWrl1b77USEhKYP38+3377LV999RUXXXQR9913HyNHjqRbt26MHm06SSUlJQEwc+ZMbrvtNgD69+9Pfn5+7euffPLJpKam1r72e++9x/333w9AVVUVW7ZsYcCAAa39NgohRMfKbaSkq3yvOfgvWmmCgaJVsOYTWBgS2MUmBTJ2gcxOMHuXlNN0YOerNpfWLlRa1BS7o66V/PaFJjjtPq7+Y6w26DnJfJx6n3ncqg/MOroP7zIfuSNNZq7/Wc3PSoZm4PLGmTLN7MHmI1RNBexZHfhZBn6mW+eaTntBNgek9w38LPvXBXbJeebsfKQEfwegeRk4T6UpwS0MlENunQtlgU6SNifkjoDxN4M7mF3rus0YDmvgOeZn/PpV8OpFMPHHcMI9HXtCoyup2GOybWs/rRfA7SkpJx1YtbuK/o1Nt3D1gOs+hU9+BbMfNicOzn+u8ZMwvuq6bH0Xs7O4ih3FlQx1p2CxdJ35hl3vtz/MTFmkha6BC9VUVsrhcHDcccfxySefMGPGDC6++GIAHnzwQbKysli8eDF+vx+Ho/EFolprHn74YU455ZRD7pfVauW4447juOOOY/DgwbzwwguMHDnykM9pTOjXobXmrbfeol+/fs3ejhBCdGqNHWTEp0H8RJMpClW+py5TV7TKBAKrP4SFL9U9Jja5QcaunwkEEruZ1u3KAvHpdHk2R10Gbut35rL7IRpmKWWCjNwRcOJvoWiNKbNc+T58fq/5yOgfCObOhG5DD5/hDM3A5R3itWPiTPlgwwYF1aVmP4pWmp/p7pWmocaSGXWPscdDRt/6ZZitKa311M1Pq6oopdF3fK3hwJZAoBYI2HYuqStxc/Uws9Hco02L/6xBLcrqVnl8XDt9Ljcd15tJfTKa/7V0pNReZtj0x3eb9Vlbf4Dzn21dRvdI4Q2cRNg8q97Nq7fvIx34YHkR/fs1MTrAFgtnPGBO1rx/h8mCTn3m4G6kPk+XPVH11oJC/v7Jalb/8VRi2yMrHyFdL4DrQi666CKeeeYZ5s2bx/Tp0wGTSXO73VgsFl544QV8PjOHIzExkdLSuhk3p5xyCo8//jgnnHACdrudNWvWkJubWy/QWr16NRaLhT59+gCwaNEi8vPz6devHzt27GDu3LmMHj2a0tJSnE4nkyZN4uWXX+aEE05gzZo1bNmyhX79+rFgwYJ6+33KKafw8MMP8/DDD6OUYuHChQwf3nU79Yh25vOYFr2lO01raPfo9jszt3UufP84jLgSeh7b9k0qRNfR63jY8FXz2pLHp0P80dDj6Pq3l+8JCeoClys/gAUv1j3GkWyCt8RunWcNVmvYneANBHBbvjdNQxKaEQRk9IWMn8Kkn5pGG6v+a4K5bx+Ab/5uMl8DzjTBXN64xssbQwM49+iD7z+c2MTGm2pUHoCi1XXZut0rYN3npnFL7XNDM7AD6wL2xOxD/53x1mXgaoIBXE0F7FhUv9lI8Guzx0HOCFOy5h5tPhIym/+1NuDx+Rnzp88pqfKyaOsBVvy+C5Yh2h1w1j9Nqej7Pw4EE0+b9ZaiacFS7r3rzPtyYjYAlsAJAp8KIxQYcgF0G2Kayrx0Hhz/K5h0V1222lvdonlvXp+f5dtLmLNhL+t2l/Gr0weQGt8+gaDX5+f/3l/Bml3m2NvaxY4XJIBrQ5MnT+aKK67gnHPOISbG/ELefPPNTJ06lRdffJFTTz21NiAbMmQIVquVoUOHcvXVV3PHHXewadMmRowYgdaajIwM3n333XrbLysr47bbbuPAgQPYbDYKCgp46qmniImJYcaMGdx2221UVlbidDr5/PPPufnmm7npppsYPHgwNpuN6dOn1zZACXXPPffw4x//mCFDhuD3++nZs2ft2jxxhNLanL0u22U+Sneacqay4OUuU2dftiswz0fXPXfczWYdQ3tY+4kpk1r2FuSOgmN+ZjqadbE/zKINWO3QbVhkthWfXlcqGKqsKKQMM3CZPyEyr9nRbA4TwPn9sPV76Hd6y7eV7IaxN5iP8r2w5iMTzM191jSsiEuH/qebMstex9YdGJbtNkFTai/Ii+D31ZliMnoNs3oV+wJB+oq60tqGGVhHcv1sXeYAs4/B4DYY9AKODR/DkzNh17KQ7FpP0x3SPdqsX8s8qk1KA0urvJRUmdesqPHxzsJCBnRLondGAnZrBEtG28OQC03G9vWr4KUpcOwv4Nift8+axq4oGMABbJ4Ng6YAYCEQwBHm9y2jH0z70gTPX/0JtnwHU542FQxhNjHx+TUrd5QwZ/1e5mzYy9yN+yitruuye8pR2Zw8sH3KgZduK+al7zYDMHlgFtYuVD4JoLTWh39UOxo1apSeN29evdtWrlwp6686Ifm5RAGf19TH1wvIQoKx2oBtV936l1DWGDMIMyEzcBn4SAxcfvN3k4WY9mX7fD2f/Ra+e9y0o575oClLyhoMk35i1lHIG/yR68VzTROJH33W0XvSNb1ykWn9PvUZeHQMnP2wyXRHUnUprP3MrJtb8ynUlJpmJX0nm8zcWz9iYf41rB54BxeP6cBuhAcF6oGPqgN1j4lLCwRymbD8bQD8tjgs7pF1wZp7dLuV127ZW8Exf//qoNtjrBYKMhMY0C2JAd0SGdgtiQHdknC1UxakVWrK4b8/NfO8eh0HU55pXlb4SLH2c3h5qvl89I9MSSQw76VfM2r9I/xq4Gf8+cIx4W9Pa5j/PHz0C4jPMN1BX7vMnHQ566F6D/X7Nat3ldYGbN9v2Ft7IqFnejzjeqUxvncarjg7Vzz7AwBH5SS1+ksOx77yGnYUV3HX5L7ccnwBqhOe6FVKzddaj2rsPsnACRGNqssayZY1CMjKdplSRxo5ieNIqQvM3KMbD84SssDpOnR2a8t38P0T4K1pnxkx3hqTKRh1LQy/Apa+CTP/AW9eY0q+jv6JOXsbDSVtonl8Hvm5t0YwA7cluP5t3KEf3xKxiSY7MGiKKcna8L+68QSBBiTfbrfwg3dHxwZwCRnmo+cxdbdpbf6mBoO5YIC31pwwmFr9O357zZUMze+Y9ZBlIVkOgPdvPZr1RWWs3FHCih0l/G9NEW8tKKy9PzvJwYBuiQzolkT/bkkM7JZIz/SEzpWliImHcx83We4PfwZPHA0XPB89We9ICTbSiUs3GbiA5MBb8msLdnHTSRV0T40Lb3tKmffYnBGmpPL508zvvzUWrTVrd5eZgG39Xr7fuJf9FabbZV5qHKcN6sb43mmM65VGdnLditBqr4+pI9wUV7ZulnG4Vu8qZUexyY5fMa5HpwzeDkcCOCG6ovK9sOZjkzFrLFsWOq8pyGKrC7yS3aa5QGJ2IHuWXRecxWeatQaR4B4Fs2tMyVDuiMhs81B81XULqa12GHaJCdhWvg/f3g//uRm+vg8m3m4CvEh9naLz89WY5haiZeyBMQJbvwdnKqT3advXs8WazFvfyXDmP2Hr9+hNs5jxRT65Pv9hn97ulDJ/TxOz6zd40JqjfvkW5Tip0R1XqtgwgOvfLZHB7mTOHZ5be1tRaTWrdpawckcJK3eUsnJHCd+u3YPXb07yxdos9MtOZEB2Ur3gLtnZ+hMjb8zbyruLtvHyj5p5YkApkwnOGW5KKqefCSfeAxPuiGw30a4suA6z9/FmTmNgJpxFe/FphR8L016cx5s3TSAhthlhQc4wuOF/8O4tsPq/fLq2mF8t+Jw9ZSYIy01xckL/LMb3Nlm23JSmxwzE2qw8cOHQVnyRzfPnD1fy1DcbSHTYSI7rmif2ukwAp7XukhFytOpspbdHnG/vN2tFwHTBC2bFckbUZc4aBmdOV/u/oeUGmgVsm98+AVwwAxfKYoWjzjUllGs/M9+7D+8y5Z3jbzVnEmMT2n7fRNuqqYAts6HgpMbv93vA0jXfqDsFW2CMwJbvTJOR9nw/tlghfwKlWaPZ9tGnZHbGAK4pSlGOOXCt8Xbcfpc3COAaa9iQkRhLRmJGvQ6V1V4f63aX1QZ0K3eU8OmKncyYt7X2MbkpTgYEsnQDAiWYealxzWrJ/rM3lwCm5K5FrdyzB8P1X8N7t5kOp1u+M9m5SA2R78qC8956BQK4LXNgwJlovwcvNu45cyB//nAld85YxJOXj2ze99/pgotf5vmn/sELWzOYNDiD8YGySLfL2WmP22Nt5liou6vrntTrEgGcw+Fg7969pKWlddpfhiOJ1pq9e/c2OQJBtANPhVljcefyzj08MynXBI/bFhz+sZHgrWq6E5ZS5mx+n5NN6/Bv/g6f3WNKLMfeBGOvN29Gomta8S68exP8bH3j64q6cJvrjjR7/R7emFfIubvKOKZiP6q8KPJr38K0M1Dy5OlKAVyIjgzgShsEcOEepMfarByVk8xROcm1t2mt2VVSXVt+GQzsvly1i0CyjvgYq8nWBQK6Ad2S6J+dSHwjGZ5dJXWNXqq9fpwxLVyr7Egy67F+eNrMLnvyGHPd3egSoiNHsIQyb5w5EbN5lun46vPiwcrx/TKwKPi/91fwwGer+dkpTYwUaIpSfB93HDFpZTx40bCI735bCAZwOSld9zi2SwRwbrebwsJCioqKOnpXRIDD4cDtdnf0bhy5tDbZhM4cvEFgFtRIWPwKnPNI2zcR8VYdnIFrbJ+CHQQL58E398PXfzaDSkdfZ7JyshC+6wmWCYW0ba/H55HBvy1w30erWLe7jAJ/NcfaAkFA3jge/WodX6zcxcSCdCYWpDM8L4VYW9v+/64N4LxdpwIktFqluhNl4FpDKUV2soPsZAfH968bcVDl8bFmV2ltCeaKHSW8t3g7L3+/pfYx+WlxgRLMujLM8pq6fbvwyTk8e9UoMpNaeGCtlDkZ5x4Jr18Nz50Kk/9oOp4eqQmA4N/EmASzpn3TTHPd58GLFZvFwtUTerBmVymPfrWevlmJnDMst+ntNfYSfo21C5WsBjuvZiRKANem7HY7PXv27OjdEC1VXGjK+OTgKXK0v+u8GQVnGJXuhOTmvSk026EycI1xj4JLX4Ody8w8qlkPmaYrI64y6+SS5SRF1xE4UNZNHCT7ato9A7ezuIp731tORmIsvTPiKchMpHdmPNlJji5RTbKhqIwlhcX85owB+L6OAx/me9htGO+//QOrdpayuLCYh79ch8NuYUzPNI4uSGNiQToDspNaVgp3CDtLul4GLrh+DKCmA/e7rCpyAVxTHHYrQ9wpDHGn1N6mtWbbgcp6JZgrd5TwyYqdBGPbYDYETGv38x6bzXNXj6ZfdmLLdyZ3JNz4DbxzE3z8C5N1OucRM/bhSBMsobTFmJmWX98HVcXg9+DBitWqUErxf2cPYn1ROT97cwn5afEM654S/kv4/dg6U4ObwyipMt+TzMTmz67rLOSIWrStkh3w4FGm+99Jv+vovYmMJW+YYPSo8zpuH7Q27fm7gpwRMH86jXa7jDRv9eEzcI3JHmS6lx3/azN+YN6zMO850wRl4o+bN/xZdLAmfs/83nYP4BZu2c/Hy3fisFuo8tQdvMfHWOmdmUBBRgK9MxPonZFAQWY8+WnxnWom13uLt6MUnDkkh3e+dZgALmc4JT4ra3aVctsJBUw7phffb9jHrHV7mLVuD3/+cBUAqfExjO+dxtEF6RxdkB5+h7tD2BXIwHVkINRcoWWTHVlC2bCJSXtRSuF2xeF2xdWb71Ve7WXVThPUrdpZwua9FVw4qjs90uK57oW5nP/4bB69bATH9G1FNYTTBZe8aqorPr/XNNO64AUzkPpIEiyhtMYGOnRq2PIdymfWwNkDgVeMzcITl4/k7Edmcv2L83jv1qPrdYo8FJOB6zoB3K4S8z3JammmtxOQAE60rd3LzeXMf8D4W9pt5k2bevtH5rIjAzi6UAAXzDRoDQv/Db1PhKRubfNa3ipTJtJS6QVw7qNw3C9g1r9gwYtmnwdNNSchsgZGbl9FZAVP5zfRYMnvrebL1Xt579WFjMx3MTLfRf/sRGxtGDB5AtmXD247miSHnXVFZazfXcb6onLWF5UxZ8Ne3l64rfbxNosiLy2uNrCrC/DiSXS0bwMWrTXvLdrOuJ6m3bffGjjQ6T6W7zfsw69hQu90khx2Th6YVXtwvqukilnr9jAzEND9d8kOwLQQN+WWaUzonU5qC+aMdcUMXGjQ1t77XVrlYdHWAyzYfIAPlmwnLsZKRY2vXfehKfGxttr/hw29e8tErp0+l2umz+VP5w5q3cgIpUw1Rfcx8MY18MxJcPrfTJVFF8iCR0R1mbm0xpgSSovdZCT9HrzaSkxI4JUaH8OzV41mymOzuP6lecy4fnxYaxJ9ft2lMnD9A9ndIe6um5GVAE60rf2b6j5/4mgzBLbH0R22OxG3azls/BbG3di+r9uVSiiDgea+9fCfW+CYn8EJv2mb1/JWmVk3rZWSB2fcb/Z1ziMmG7f0DTNMeNJP26ejpmihxgM4r6eGbRU+vlq1m/cWbwfAabcyrHsKI/JTGJnvYnh3V0QHGHsDB+w2i4XMJAeZSQ4m9K7/+1lW7WVDURnri8pYt7uM9bvLWVdUxperdtcrv8tKiqWgNltnLntnJJCVFNsm5ZjLt5ewYU85047pBYAOZrbzxjNrzR4cdgsj8lMOel5WkoMpI9xMGeFGa836ovLagO6Dxdt59QezHuqonCSOLkhnQkE6Y3qkhnWQuKs2gOs6a+BCs4VtmYHz+zUb9pSzYMt+Fm7Zz4LNB1izu9QUayjom5nILccXcGzfDBZuPdBm+xEJOSlO3rhxPLe8spC7317Kln0V3DW5X+tKcvPGwY3fwtvT4P07zDy0M/4R3R2I/T6Y+SB61j/Z7eiFr9RDTorTlJdumoXyp1GDjaQGa9f6ZSfy0MXDmfbSPH725mIevmT4Yf/GdLUM3LUTe3JC/0x6ZXTdn78EcKJtFa02GZFrPjRnv144C477pTkIbuuGFm1Na3NQP/NB09mwHcvs/H4fXh/s3l9BYqyd+Fhrm2YSWifwRz04QLRoddu9lLe6eWvgDicxCyb/AY6+E75/Er5/HFZ9AL1PgEl3QY+JkXst0UqHzsBVV1fjxcb0a8eQnexg/ub9LNi8n/mb9/PE/zbgCwRLvTPiGZnvYkSeyQ70zkho8YFjMAA71IFNQqztoHVDYLI1W/ZVsH53WSBzZwK7txdsq1cOlxBro3dGfEgpprnMT4trVTnmfxZtw25VnDYoG4DNzoEsqR7OkB5HM/ujhYzukXrYpiVKKQoyzT5dNaEHXp+fpduKawO652dt4slvNhBjNcHg0YGGKINzkxv9e7ajtomJn417yvH5/cTarDjsVhx2Cw67tVOVoELblVCWVnlYvLWYBVv2B4K2AxRXmnU9SQ4bw/NcnD64GyPyUxjaPYWkkAzuoNzOn3VIdNh59qpR/PY/y3ns6/Vs2VfB/RcMxWFvxXFDfDpc9qZZ7/z1X2D7IrjwRchsZtfFruDAVnjnBtg8i7LeZ3Hy8rP40fxCbj+xj3nfmvUQ9pTRVGDFZj3479NJA7P4+Sn9+evHq+iXlchtJx567qPPr3HYO9f/vUOxWFSXDt5AAjjR1opWQUY/6DbUDHz84Cfw1Z9g07cw5Wkzq6yr8nvrDhaXvQ3H/qzdXnrljhLii6s57q9f1d7mtFtJcNhIiA35cNhIDFwefN1OQqyNxJD7EmJtxNoskT2jrxoEcHvWRG7bDfg8VewshzSPr3Vv9A3FpcLxvzRlwPOehTmPwvTTIW+8CeQKTuw6GdFoVVtC2fhBsh0vHmw47BZyU5zkpjg5e2gOABU1XpYUFtcGdZ+u2MXr8woBczA8It/FyDwXI/JdDO2eEvawW28gU9SSoMJutdRm2SbX+zI1u0urQwI7czl73V7eXlC/HDM/Le7grF1mwmH33+/XvL94B8f2zSAlzmQk98X14le+P/CcJ4Y1u8o4b3jzG/zYrBaG57kYnufi1hP6UFHjZe6m/cwOBHT3f7qG+z9dQ2KsjXGB9XMTC9LpnRGPUqo2A1da7eX4+79u9DWsFoXDZgkEdVZi7RYctroArzbYs1mJDQn86j/GXMY28bz6jzn038t6GbgWllBqHciubd7Pgi0HWLhlP6t31WXX+mQmcNqgbEbkuRiRn0Kv9JafdOhM7FYLfz5vED3S4vjLR6vYUVzFU1eMJC2hFSfpLFY49ufQfSy8dR08fTyc+SAMvThyO97Rlr0NH/zYZODOfYLN6adRsnwW323YawK4/Anw7QNkFS9hLelNnmC68dherNlVygOfraFPVgKnDmp66UNX60IZDSSAE22raHXdYN3YRJjyFPQ6Fv57Fzw+0VwvOLFj97GlfB5qz/ove6tdAziv1wvKwt/OH0JZlZeyavNRGvy8ykNZtZet+ypq7yur8tYryWqK3apCAjp74wFgyG2JwWCw3nUbcTFWc2ATLKEsnGsu964Hn7dNupJWV5Tzv70lPHDfl1w1oQeXj8tv0VqbJjmSTDZuzA2w8CXTtfLlqdBtmMkq9z+z/Yeli7CYAK7xoD4uxsa4XmmM65UGmAPmjXvKTUC3xWTpvl5txthYFPTPTqpdvzMy39XkwNrSQKezBEfkfteVUmQlOchKcjChoH45ZmmVhw1F5aYUM1CSuW53GV+srF+OmZ3kCAR08fUCvIxEU475w6Z97Cyp4ldnDKh9jiPGSmWNjznr9wIwsSCt1V9LXIyNY/tmcGygUcXesmrmbNjLrHV7mbVuD5+t2FW7vxMK0thTVoNFgV+bzoV/O38INV4/VV4/1R4fVR4fVR6/ufSGfO7xU+0195dWeQ+6v9rjb1VjlFhb/cAvGAzG2q21mV0If4xAWbWXxVsPBAK2/SzceoADFeZ3KTGQXTs1ELANy6ufXYs2SiluOLY33VPjuHPGIqY8bjpU9m5t9qTXsXDjTHjzukCmajac9tfOP5rnUKpL4cOfm7E97tHm+Cq1F54t+wFYsGU/NV4/Md3HgrIS4yvHQ1aTa9eUUvxlymA27innzhmL6Z4aV28eYKiu1oUyGkgAJ9pOxT4o2wUZIeUJSsHwyyF3FLxxNfx7imkOcfyvu96YAV9N3edFK2HXivZrcqH9KKW4cFT38J+iNdVef0iQ56W02tNEABh63cPu0io2FNXdFs6BiEWZ8q6nh+1jLJj1aRkDzPdq/0ZIP3RJRktY/NVUY2do9xT+8dkaHvt6HReM7M51R/ekR3p85F4oJs7MFRp5DSx5zZTRvn6F+V2f9FM4akrX+32OFo2VUPp9WJXGo22kh3H2XilTXtMrI4ELAv/HiitNQ4hglu7tBYW89N1mANITYhkZWEc3Mt/FUTnJOOxW9pXXEGuzEN/SwcTNlOgwv/tDG7T/9vj8bN5bURvYrQ9k7t5qUI6Z6LDROyOBihovcTFWThpQN+PLabdS5fEza90ekhy2Jg/kWiMtIZYzh+Rw5hCTGd2yt4JZ6012LhhAF2QmsGZXGUcXpDd7VtWh+Pw6EOQFg77A54HAr7pBYFhZU/d5dSOPD27H69eMyEthwZYDjZZQBk8WLNhywJRDbt7Pml2ltQOx+2QmcMrAbEbkpzAir3UlvV3Z6YO7kZXk4PoX5zHlsdk8dcVIxvZq5UmExGy48j9mDui3D8C2BXDhC12z63DhPJNRPLAFjvm5yTJaTWAf/L2r8vhZUniAUT1SIWcYbJuPF9shS7wdditPXTGSsx+ZxbQX5vGfW48mo5HW+15f12piEg3kCEO0nWCpXEYj9eWZ/WHal2Y+y8x/mLNf5z/bteZuBUsogxmmZW+1a5dCP837Y6mUqi0FauwPcHPUeP2UNwz6qj0HBYBvzS/kh037TQAHMPIq+Phuk5lN7wPeGpOZc6ZAYjfT9rkVpYg2fw1eSwzPXT2atbtKefrbDcyYu5V/f7+ZU4/KZtoxvRiRd3DXs5a/YAyMuBKGXgor3jUHAW9PM2XCR98JQy+J7Jo8EYZGArjAHCRbTEyL20YnO+31skU+v2b1ztLag+75W/bzyXKTMYqxWjgqN4niCg/pCW3TZKQ57FZL7Vq0UFprdpVU1zVQCVzuKK7ksrF5xMXUHSI47VYqarzMWreX8b3T2qVhQV5aHHlpeVwyJg+/38wT+8MHK1izq6zeAOlIsFoUcTE24tpo0sTg331S+3dz8dZAsBYoh9wfzK7F2hiWl8IpR2UzIt/FsO4pJDujN7vWXCPzXbxz80Sunv4DVzz7A387fwjnDm9lEG+1wYm/he7j4J3r4clj4ZyHO7jLdDP4ffDtP8yavqRcuPpDyB9f7yGh2eXvN+4zAVz+BNg2Hx/Ww/59ykxy8PSVo7jgydnc+O/5vDJt7EHrX31+3ehaOtF2JIATbafIzAMio1/j98fEwdkPQ49jTL32E0fDuY9Dv9PabRdbJZiBszlMPf2yt0x3xXY4WFPaj+7AMQIxNgsxtpjDduyr8vjYMKfS/KWxOWDwhSaA27MaOBOWzID3bq17gs1pRgwk5gQuu0FSTv3LxOzaM4v1aI1N1+BVJmDqk5XI384fyl2T+/HCnE28NGczHy3byegeLqZN6sVJA7IidybbaoPB55vM25qP4Jv7Taezr/9qWliPuMr8vkerA1vM+tapz5hgvCMcaoyA3xwgxzsjN/PHalEMzEliYE4Sl4/LB6CotLq2qcSCzfspPFDJhN6tLzVsK0opspMdZCc7mFhw6O6tzhgr+ys87K/wcMOxvdppD+tYLIruqXGc0D+TT1fs4tRBXWv9dIzNwoy5W5g+e2Ntdq0gM4GTB2YF1q65KDhCs2vNkZcWx9s3TeCGl+bz4xmL2LKvgttOKGj9SZK+k+GGb+HNa0x10OY5poFVZz4Bd2ALvH09bJkDg86HMx5o9O9vMANnsyi+27CXW44vgPyjYfbDeMMMAwa7k3nggmHc8soCfv3OMv5+/pB633OfrIFrdxLAibZTtBrscZB8mDK/IReYtuxvXA2vXgzjboaT/s9kNzqz0DVwg6aaQGT7wpa3mD+wxXyvwngjUtrfHmOxW21CQTpvzwrsae4oiE8zwVlRIDtbamZEMfVZKN1prpdsN5dbfzC3BYeQ1lIQn9Eg0MuBBJMZ8Vrq/95kJjn42Sn9ufm4Al6ft5VnZ27k+pfm0ys9nusm9WTqCHfkGp5YLND/DOh3Oqz/0mTkPr7bBHTjb4bRPwJH5+8AF7aSHSZruvUHWPcZbF9gOnR2pMaamAQycNrSttmMjMRYTjkqm1OOMsFFjTd61oWE/h9pOAqhPV08Jo/zR7o7cdfdxp0+uBub9pYzPM/FiLwUhnd3kRwn2bWWSImL4aXrxnL3W0v4x2dr2Ly3gr9MGUyMrZW/EyndTQbr83vhu0dNdcgF08GVH4ndjqylb5qTZtoP5z0FQy9q8qHB+YMj8lzM37wfj8+PPW8cGoVPhR8GnDGkG2t29eGhL9bSPzuRH02qO5Hj7WJz4KKBBHCi7RStgvS+4TV1SOsNP/ocPr0HvnvMnFE6/zlIbf8zvWHzeQJn+xUMOBM+uNNk4VoSwG2bD0+fYLphjbo2jCdoNJ3/AGZMj1TeCf7888aZy4y+gQwcULkfYhJN9qoxWpu1lKXbTbDQ8LJ4K2z9Hir31T6lzNJ4gBQfa+OaiT25Ylw+Hy3byVPfbODX7yzjH5+u4crxPbhifAQbnihlmvMUnGjO5H57P3zxe5j5EIy9HsbeZILZrqxiHzwyyqy1CAalB7Z24A7pBpchagO49j0p1OoDyk7EGQjgspJi6Z0RwfWkLdDVgjeAP5w7qKN3IarE2Cw8cOFQ8tLi+Ofna9l+oJInLh/Z+qDYFgOn/tmUIb57Mzx5DJz3ROepDKoqgQ9/ZtZeu8fA1KfB1eOQTwmuWZ/UJ50fNu1j6bZiRuS52JwwjN1lzXsfuuPEPqzZVcqfP1xJ74yE2lJmXxebAxcNut5fQdF1FK1ufP1bU2yxcPrf4KJ/w74NphZ92dttt3+tFSjLAkwWouBEWP4u+FvQzWzvBnMZbLV/WBrdzDVwHSE+1kZ+eiIAPvdYHv5iLcUJvWDPWhOcVe4337umKGUCnezBpsRl5NWmnf/ZD8Plb8JNs+AXG+HXu+D2Rfy1+2N85TzpkPtks1o4a2gO7906kdeuH8fQ7ik8+PkaJtz3Bfe8u4xNe8oj+B3AHAhc/hZc/zX0Oga++Tv8czB88muTYeyqFr8GNWVQecB0PwOTRe4gm/eZn1tVjffgOwPlzloay7SYMzDjaWJBeoev6RMCTAnwj0/qyz8uHMq8zfuY8vgstu6riMzGB5xlRh+l5JnKoM9+W3siqMNs/cEsNVn6upmne81Hhw3eoK6E8ug+JnP+/QZzwvO5ng/wN8u0Zu2CxaJ44MKh9M9O4vZXF7J2l/nb75UulO1OAjjRNqpKoGRb0+vfDmXAWaYWPb2vqUf/4E7wVEZ+H1sruAYueDAzaCqUFELhD83fVnCoeZhvEKaEsmv8sTyQPYF/eM7nV4tSeeCzNcw6kGYO/Eu2BwK4lNa/iN0BqT1ZrvrgdIS3zkkpxbheaTx39Wg+u/MYzh6aw4y5Wzn+ga+56d/zWRBovRwxOcPNyYmbvzcZ2+8eh38OMWUw+zdH9rXamtYwf3rgcz9Ul5nPizsuA1dUYkptS6sb+T8UPNnSxiWU0cwZ6KQ5sQPLJ4VozJQRbl68dixFpdWc99gsFkbqb3dqL7juM1MVM+sheOEs877V3nxes576uVMBDdd8DMfdHXanY09gHmW3ZCcFmQl8t8GMAqnBjrI1/6RWXIyNp68aRazdyo9enMf+8hrJwHUACeBE2zhUB8pwuPLh2o9hwu0w7zl45qS6dVOdha/Bmf5+p5lGHcveav62gk05vFVhPbwrBXDD+vTgX74pzFhoOvTNLg6UbOxZffgMXDOVV3uJD3PIcqhgw5OZvziem4/rzez1e5ny2GzOf3w2ny7fiT+M+Xlhy+xv5vPcNh+GXQILXoR/DYd3bjKZya5gy3d1ZbBoE5BDpyih1I39rIInRhprfiPCUpCZSLdkB8cEunAK0ZmM753G2zdPJC7GxsVPfcfHy3ZEZsN2h1naMOUZ2LHEZMDWfxmZbYdj/2aYfoYZdTD4fDO7Lm/s4Z8XosbrA0zZ6bheqczbtA+vzx9Yt9ayMCA3xcmTV4xkx4EqbnllAdVRtN63q5AATrSNQAfKqpQ+nPjA19z/yermHwRb7aYL1GVvmqYWTx0Li15tg51tIV9N3Ro4MIPK+54Cy985OLg7nNgkc7nmY3j3FjNT7jA6sgtlcwRbC/fLSuTqCT34fE9gvVTRmshl4AJaGsAFBRuezL77BH531kB2llRx/UvzOekf/+Pl7zdT5fFFbF9J7QlnPQR3LIYx15vfm0dGw+IZkXuNtjJ/ulm7aLGb/wOdoISydu3bIZqYYO3kjZE6sZH5Lub88sRWjyARoq0UZCbwzs0TGJiTxE0vL+DpbzagG+tK2xJDLjBl8PGZ8NIU+OrPpoV/W1ryugkYd68wAeSUp1rUBCs4RsBuVYztmUZ5jY/l20vw+vytypqNzHfxlymDmb1+L6VVXulC2c7kuy3aRqC74JLSeNYXlfPIV+u4Y8ailh0A9znZnHXKGQ7v3mgyFcGSrY4UOsg7aNBUKC+CzTObty1HIIBL7g7L34bHx5s3ifVfNdoWXdF1MnD9s5M4KieJRy8bztEF6ez0JeGNSQ5k4A5ENANXVu0loRUBXFCw4cnXdx3Hw5cMJz7Wxq/fWcbE+77koc/Xsq+8kZ99SyXnwmn3wY+XmlLa3csjt+22ULHPBJtDLjQBkfbXZeBKt3fYOpHgfxN/Y2tQA/9XlZRQChHV0hJieXXaOE4f1I0/fbiS37y7DK+vBevSG5PRF6Z9YeZ7/u+v8NJ5ULY7MtsOVVUMb00zM0UzB5rjnyEXtHhzwTVwMTYLY3ulAvD9xr0mA9fK2W1TR7q54RjTbK4L9hbq0uTbLdrUosISAG44thfvL97OFc9+z/6WHPwm5cCV78Gxv4DFr8LTx8POZRHe2+bRwTECoQv6+0yGmIRml1Fu22/W+C0Z8mu4c7mZJ7dzKbx0rjkDt+hVM/Q6oCuVUPbLTuS/t0+iIDOREfkuQFHk6AFFa9CV+9la6WBJ4QF2Fle1+o3WZOAiNBKAdm54kpABFlvjc8w6kyWvm9EOI6+u+90PZuC0v2PWiADqUF0o/SYjrjv7aBIhRKs57FYevmQ4Nx7bm5e/38KPXpxHWXUzq2KaEhMP5z0O5zxqOiA/MQk2zYrMtsGUpz9xtDmGOP7XcPV/WzXGYE9ZNV+vLsJuVcRYLWQmOuiVHs93G/aZ4dsRKHv8+an9uf2EAs4cktPqbYnwSUsu0aYWbj1Az/R4fnnaAAbnJvOT1xcz5fHZTL9mNPlpzWxFbbXB8b+C/InmzNQzJ8Kp99U/kGxHfm81B4UKdqeZAbbiPTj9gbBn2a3ZVUwu8N3GfQw5MRWO+ZlZ/7f0DZj9iMk8fvF/ptRu1DUAXSaAC5UaH0Ov9HjW+XPotmMmyu/hpcXFPLXAvAEqBWnxMWQmOshMiiUzMbbe5xmJjsBlbKOz28qrfSTERj7LEmx4Mq5XGmt3lfLMtxuZMXcr//5+M6cMzOb6Y3sxIi8SmURFowFIZxFsXpIzAroNAVSghLIMlBW0z5RRdsDcpNrwrbEAOJiBkzVwQhwRLBbF3af1Jz8tjt+8u4zzH5/N89eMpluyMzIvMPxy6DYM3rgKXjgTTrgHJv44vLFJjfF5TYfib/5mKnGu/Ri6j2nVLn69ejd3vbGEkioPfzhnUG332LG90vhg8XZG90yNSNmj1aL4yeQWNKwTrSIBnGhTC7ccYEIfM9T2zCE5ZCc5mPbiPM57bDZPXzmKkfktOOjtdawpKXj7evjgx7Dxf2YtUTsPSPZ6PFi1JmQVnDFoqmnzu+Fr0/o+DJbAH9Z6CShbrHmTGHYZrPsC5jxsgrhv7ifPZ2GjckfoK2lfI/JdzF2RwSRtsjZlKpGnrhjJ7tJqdpdWU1Raxe4S8/nKHSXsKTMdrhpKdtpNgJdkgryMxFhqfH4SIpiBa0yfrET+ev4Qfjq5Ly/M2cS/v9vCx8t3MirfxbRjenHygCwsLT2rqVTnzsBt/QGKVsJZ/zLXlSVQQllqZjnuWdNhnShrv+OHCOAsEsAJcUS5ZEweOSlObnl5Aec+Oovnrh7NUTkROlbIHgTTvoL37zDvzVvmwHlPQlxq87azb6M5nin8wZRnnva3umUVLVDt9fG3j1fz7MyN9M1K4N8/GkP/7LrtjeuVyqs/bGHptmK6JYfXtVl0PhLAibYROIbaU1bN8LyU2ptH9Ujl7Zsncs3zP3Dp09/xz4uGcdrgbs3ffkImXP42zPonfPlH2L4Qzn++ZUO0W8jnqWbNrlJyqn3YvT5ibYHAofcJJphc9lbYAVywDL3RQ3eloM9J5mPnUpjzKI7Fr1NiS4zI19HeRua7+GRhFgSTk3EuJh+V3eTjfX7NvvIadpdWmQCvpJpdJVWBgM9c/rBxH0Wlpo18j/T2GTIcbHhy83EFvD5vK8/O3MgNL82nZ3o8P5rUk6kj3I1mCQ+tk2dVg81LBk011xVAIAOXPcgEcB3ViTIQuPkbbWJiyqeUNDER4ohzbN8M3rhxPNdOn8sFT8zh0UtH1A6gbjVHEpz/HORPgE9+ZQZ/XzAd3KMO/1ytYckM+O9d5mTY1GdNp8lWWLe7lNteXcTKHSVcOT6fX50+4KD3obE9TSfootJq3K4IZSRFu5MATrQpjWJ4g9KynunxvH3zRKa9OI+bX1nAr04bwI8m9Wz+cFiLBSb9xPzhfPM6eHay6Vo59sZ2Kan0eWsoqawhS2u+XrGrrv7bFgMDzjZDvT2VpqzyMIIJm8MuAcseDOc9wc+Lz2fjvho68ZjzJo3Kd/Gozq29bo8/9NlKq0WRESibPOoQj9NaU+nxERfTvn/Wgg1PrhiXz0fLdvLUNxv49TvL+Mena7hyfA+uGJ9PanyYgUMwo9UZVe43DXaGXQqxCXh8fqpr/OzdW05+dSnEpUNCNhR3VCfK2i4mB98VLKG0SwAnxJFoQLck3r1lItdOn8t1L8zl/84+iivG94jMxpWCMdMgd6QpqXzu1MMfi1QegP/+FJa9CXkTYMqTZmh4C2mteeWHLfzhgxU47VaeuXIUJw3MavSx2ckOeqTFsWlvhbT+78KkiYloUw67hX7ZB2eKUuNjePlHY2s7Rf32P8tb3sAibxzc+C0UnAQf3w2vXWo65bUxn7emNmP22g8Nsg6DppqysrWfhbWtWJv5I7p4WzGeML4PpZYUKiwJzdndTqN3RgKlsdnUKNOO3JEUmcHASql2D95CNWx4MqwlDU86cwnlkjfMnMKRVwPw3yU7qPFp9pZWoqvL+HJjJb4kdweOEgjMgWskj60DnTEt0sREiCNWVpKD128Yzwn9M7nnP8v54wcrGi3Pb7HcEXDDN6aZ2cd3w+tXgKeR2a6b55jmJ8vfMQ3Lrv6gVcHb/vIabvz3fH79zjJG5afy8Y+PaTJ4Cwpm4Vo6B050PPnJiTpamwGVxYUR2+TgnBTsTfSWDXaKuuHYXrz03Wauf2k+5S3tFBWXCpe8Cqf8xQRNT0yCLd+3Ys8Pz++tQaHRKGau28OWvRV1d/aYBPEZYXejTHaatTkeH0x7cR4VNYf+Pvg1zc9YdhIWi2J4fhpblMlYxqdEJoDrLIINT569ejSf3XkM5wzNZcbcrRz/wNfc+NJ8FmzZf6hn0ymbmGgN8583ozy6DUVrzZPfbECjsGgvylfFol1e9tuzOqyEsjbubSQA9nlNea1FxggIcUSLj7Xx5BWjuHpCD56ZuZGbX55PZU0E57k5XXDxy6aD5Mr3Yf0Xdff5PGbJx/TTzciY6z41DcssLV+3PXv9Hk576Fu+XLWbX58+gBevHUNW0uHXtQXHCbR2jIDoOBLACaNoNbx4jplr8vSJZshyK3gDAy6H5R16sbDFovjlaQP447mD+Hr1bi58cg67Sho5YxUOpWD8zeaPotUGz58G3/6j8ZKqCPB76tr6WxTMmBeSebDaYOC5sOaTuhbrhxI46BzWPYVv1hRxyVPfsbes+lBP6OyrpQ5pZJ6L5R6z9jEpNUJrETqhYMOTmb84npuP682cDXuZ8thszn98Np8s33nwcHtF58zAFc41w2QD2beZ6/awckcJMXhJq9wEQBlOimzZ5gRQG/2fO5Tg/4fGulD6AiM4LHYZQi3Ekc5qUdx79lH87qyBfLpiFxc/NYfdpS087miMUmYZBUDg5BH7NpjSym/+bhqV3PhteOvkmuDx+fnrx6u47JnviYux8s7NE5l2TK+wG2iN7WUycK0Z5C06lgRwR7rqUvj0Hnh8AmxfZM4aaT9MPwN2rWjxZncHGkoMC7O1+uXj8nn2qtFs3FPOeY/OYvXOMIKepgTLGAaebTpDvTwVyopavr0m+H01gYNtxfH9MnljXmH9MtBBU8BbCas/Puy2ggedx/XL5InLR7JqZynnPzGHrfsqGn28X7e8W3FnMDLfxTe+ISzx9yTTldLRu9Pmgg1PZt99AveeNZCdJVXc8NJ8TvzH/3j5+811A+6VhU6ZgZs/3cw3DDQveeqbDWQmxpKoKuleugiAMhxs02ng90DZznbfxeD/oUYDuMDJFotNMnBCCOOaiT156opRrNlVxnmPzmbtrlYcdzQUrJDRflj0iqkK2rvWNFs79zGIbXkTsk17yjn/8dk8/vV6LhrVnQ9uP5pBuc3rrJmb4qRHWhzxHbjsQLROFz4EFK2iNSx9Ex4ZDbP/BUMuhtvmw7E/N4MjlcXMNtm5tEWb31lszmYNc4c/JuD4/pm8fsN4fFpz/uOzmbl2T4teGzBdIM9/Hs580AzZfGIibPym5dsL4dfmD3NoBu7iMXnsLq3my1W76x7YfRwk5pjGD2FSSjH5qGxe/tFY9pXXMOXx2SzfXnzQ47TWqC6cgxvaPYV39DGcXfMnsiI1l6cLiI+1cfXEnnx913E8culwEh02fv3OMibe9yUPfb4WP6rzNTGpPADL3jbd0WITWb69mG/X7uGaiT3rPaxcO9noMWd1O6aMMhC4NfL98wcycFZpYiKECHHywCxm3DCOGp+fKY/PZta6Vhx3hFKBw+u3roN3b4JuQ+HGWebEbgtprXlzfiFn/OtbNu4p57HLRnDf1CEtXvv9zFWj+eXp/Vu8P6JjSQB3JNq9El44y/xhic+A6z6Dcx+FhAxzf0ZfuOZDsDlh+pmmRX8z1XjNQVRaQvMOmAblJvPOzRPJdTm5+vkfeH1eKw4ElYJR18K0LyE2CV44G776C/hbV+/uC/y38QcaI2gUx/fLIDMxltfmhuyvxWL+WK/9zHTwO6T6WYNRPVJ588bx2CyKi578jtkN3lQ0dZ0ru6L4WBsDupm5NEfiHBqb1cKZQ3L4zy31G54cqPCwdV8YzU7a09I3TCY5UD751DcbiI+xcunY+ovuy3GwojLFXOmgWXAA+lABnDQxEUI0MMSdwjs3T6BbsoOrnmvlcUetkDfoE38LV70PKd1bvLXiSg+3v7aIu95YzFG5yXz842M4vSUjmEIUZCbgdsW1ahui44QVwCmlTlVKrVZKrVNK3d3I/XlKqa+UUguVUkuUUqc3cn+ZUuquSO24aIGqEvjk1/DE0SazdsYDcP3X0H3MwY9N6w3X/NfMOHnhHNg6t1kvpVpRBpaT4uSNG8czvncaP39zCf/4dHWjZVFhyx5kvs6hF8P/7jNr/Up2tHhztb0SvDW112xWCxeMcvP16t3sKK6se/CgKaakbNV/D73N4FqokMYkfbISefvmCeSkOLjq+R94f/H22vv8mnYZldCWRvdIxW41IwKOVKENTz7/yTFoFEWRXIvRWlrDvOfN2eOc4RTur+CDJTu4ZExebeOdoDLtYHFJoCzowOaO2FnzbyNd5XyeYBfKI/d3TQjRNLcrjjdvmlB73HH/J6087gh9f57001Y1Kpm3aR+nP/QtHy7dwV2T+/LqtHHkpBw5lSuicYcN4JRSVuBR4DRgIHCJUmpgg4f9Bnhdaz0cuBh4rMH9/wA+av3uioNU7od1X5iFsa9eaoKT9V/Wf4zWsOR1Uy4551Ezx+m2BTD6R4f+o+LqAVd/aDo8vnSuaX3bThIddp67ejQXjerOv75cx09eX0y1txWZs9gEOO8JOPdx2DbflFSu/bxFm7IEDxR9HtB1TcsvGpWHX8Mb80K6eOaMMN/Hw3ajPDiAA+iW7OSNGyYwrHsKt7+2kOdnbTSP1l27iQnA7Sf24d/XjW2yS+mRpiAzMVBC2dF7EmLbfNi9HEZeA8BzMzehgGuP7nnQQ8uIY1MpaGdqx5RQBisoG/kG+gONBKx2WQMnhGhcUuC44+LR3Xnkq3Xc8dqiuvXJzRWBE6xen59/fr6GC5+cg8UCb9w4nltP6CONRwQQ3iDvMcA6rfUGAKXUa8A5QGiHCw0kBT5PBmpTBUqpc4GNQCerC+qCaipg5xJzULVtAWxfYDobBaUVmJkjL50HPY+Fk+4FWyx8+DPYPMu0AL/4FXCPDP81U7qbcsoXzoZ/T4FLX4eekw77tNoTV634I2a3Wrhv6mDy0uL4+yer2X6gkqeuGEVyXCsOwoZdCrmj4I2rTXOTiXfACfeANfxtBrOL2ltT7/a8tDiOLkhnxtyt3Hp8gekGpZRp/DDzn6aRSrBMtYHab1cj9yXH2XnpurHc/upC/u/9FewurcavdZcuoQQzCzDYCUsEdbI1cPOfB3s8DD6f4goPr83dwtlDcxo9+1uGA7+GmkQ3sR1YQtlYF8/gHDi7lFAKIQ7BbrXwlynmuONvH69mR7E57nDFN/dvR+veoAv3V/Dj1xYxb/N+zhuey+/POYpEh5yAEnXCCeBygdB340JgbIPH3At8qpS6DYgHTgJQSiUAvwBOBqR8sjl8HtO2OxiobVtg1q7pwNmgpFwTkA2/wnRd7DYMnCmmZe285+Gbv8HTx5uFtI5kOPOfMOLKlqXxk3JMY5MXz4aXL4BLXoHeJ4T11NbGGEopbjm+ALfLyc/eWMKUx2cx/ZoxdE9tRd12Rl+Y9gV8/EuY9RBsng3nPxf2IE2rCsnAUb+h/8VjunPrKwv5dt0eju0bCNYGTYVvH4CV/zFZz8YEO+g18R1z2K08fvlI7vnPMh7/ej0WBcPD7PApug7z8+8kAVxVcaB5yQUQm8i/v1pHRY2Pacf0avTh2h4PNVDm6EbsgY3tvLOhO9L4GrgabcVua3kZkxDiyKCU4ubjCshLjeMnry9myuOzef7q0fRIj2/ORlr8+u8t3s6v31mK1vDPi4Zx7vDcFm9LRK9I9Q+9BJiutX5AKTUeeEkpNQgT2D2otS471NBhpdT1wPUAeXktn0bfZfn9sG99/WBt5xLwBtbCOF2mFK/faZA70nyemNX4tmyxMO5Gk2n6/kmoKYWJPzZlkK2RmBUI4s6BVy6Gi/4NfSc3+fDWrIFrzDnDcslOcnD9S/M577FZPHPVaIZ1T2n5Bu1OOOufJpv43h1mXeA5j8GAMw/9vNCz+76ag+4+eWAWqfExvPbDlroALnMgpPczB8NNBXCB75dSTZcTWi2KP507iKxEBw9+vqbLZ+BEIzrTHO+lb4CnAkZeTZXHx/OzNnFM34za5jMN5WZlsm1rOXusWaQd+Mb8X2nXdZpNjxHwez14sGG3SbmuECI8Zw7JoVuygx+9MI/zHpvFU1eOYnSPMI+lDvFe3pSyai/3vrecN+cXMjwvhYcuGk5emjQZEY0LJ4DbBoS2znEHbgt1HXAqgNZ6jlLKAaRjMnXnK6X+BqQAfqVUldb6kdAna62fAp4CGDVqVGc5fGkbWkPJdlMGGQzWti+C6kCreHucyaaN/pHJsOWOAFfP5h8IOZLg2J9Fdt/j000npZfOhdcuhQtfgP5nNPrQtvghju2Vxts3T+Dq53/g4qfm8M+LhnPqoOzWbXTQVPN9fuMamHEZjL0RTv69CYQbE3p23+cBrQjNM8barEwdkcvzszZRVFptGnQEyyi//ov52SflNLZhc3GYH7NSijtO6kOfrAQcdjkYjTZ+LK1bOB8pWsO86ZA9BHKG8+7crewpq+aGJrJvHqz0yk5lXmE528mgn7cSKvaavxnttstNB3DaV4MXK3arnPUQQoRvZH4q79w8kWunz+Wyp7/n/guHcvbQxt7DG2re35rFWw9wx2sL2bKvgttPKOC2E/vI+nBxSOH8dswF+iileiqlYjBNSt5r8JgtwIkASqkBgAMo0lpP0lr30Fr3AP4J/Llh8Bb1KvbBus/hf383masH+sGDA+H1K2D2w1B1AAZPhbMfgZtmw91b4dqP4JQ/mblLqb0OG7ztK6/hxTmbOOfRWRzzt6+oqPG2aFerPD4e+nwtny4/xBDeuFS48j3Tle71K2H5O4fZamQPmHpnJPDOzRPpn53ETS/P59mZESjVSu0F130K426G75+AZ0+Gvesbf2xIABcsoWzootF5eP2atxaENDMZNAXQsPzdxrfbzKq50wd344T+TWRhRRfWSUooty2AXUth5NX4NTz17QYG5SYxoXfjaxbLtQNXfAzZSQ42eANnqNu5E2Vt1r+xANjnoQabHBAJIZqtR3o8b90UaCb26kIe/Wrd4U+0BY/bDpOJ8/s1j3+9nqmPz6bG6+fVaeP4yeR+8rdKHNZhM3Baa69S6lbgE8AKPKe1Xq6U+j0wT2v9HvBT4Gml1J2YVMLVulOcRm5nNeWwY3FIKeR82L8pcKeC9D7Q63hTBpk7ArIGgb1lM7CqvT6+WrWbtxZs4+vVu/H4NL3S49myr4L3F2/notHNK0VduaOE219dyNrdZQBcMS6fX58xAIe9kTUjzhS44h2zHu7Na8HnhSEXNLrdQ5XOtlR6QiyvThvHj2cs5A8frGDrvgruOXNg6zoz2WLh1L9Aj0lm6OaTx5oSy8Hn139caAbOXwPYD8o2FmQmMLqHixlzt3LDMb3M9yC9j8lmLHsLxt/cyA4ESyglQ3Ak07X/dLD5z5tqgMEX8MWq3WwoKudflwxv8vezHAeJDhtuVxyrgrPgDmw1f+vaWWNz4LS3Bq8EcEKIFnLFx/DSj8bwizeX8PdPVrN5bzl/Om/wIf6mBAO4ptfd7iyu4ievL2L2+r2cPjibv5w3pHVN2sQRJaw1cFrrD4EPG9z225DPVwATD7ONe1uwf52Xt8a0164N1hZC0cq6A/zk7qY0b+TV5iCm2zBT1tgKWmsWbDnA2wsK+WDJDoorPWQmxnLNxJ6cNzyX/tmJTH7wG175fkvYAZzfr3l+9ib++tEqkuPsPHf1KOas38vT325k3ub9PHrpcHplJBz8REcSXP4WvHoxvD3NzDobdmnt3ZFeA9eQM8bKY5eN5C8fruSZmRsp3F/Jvy4ZRlxMK5d19j8dbpxphpy/dR1s/AZOvQ9iAnXoIQeHKpiBa+SY9uLRefz0jcV8t2Ef44NZi0FT4fPfmaDe1aPe4+u+WxLAHcl0Z+hCWVViTjQMmgqOJJ783zLcLienH6JcuUI7SIy14XY5Wboh8HeuvTtR1ibgGgng/B68WkoohRAtF2uz8uBFw8hLi+dfX6xl24FKHrts5EEzMYG6zFsTjeM+Wb6TX7y1hGqPn79NHcIFo9xyAlc0S6SamBw55k+HBS+ZQdg+M1uIuDTTWGTAmeYydwQkZEbsJbfsreCdhdt4Z2Ehm/ZW4LBbOPWobKaMcDOxIL1e5unSsXn83/srWLatmEG5yYfc7u6SKu56cwnfrCnipAGZ/HXqENISYjmhfxbjeqXx0zcWc+bDM/nTeYM4b7j74A3EJpixAq9dAu/ebJp6jLwaaLyKKdKsFsVvzhxI99Q4/u/95Vz81Hc8c9UoMhNbltWsldLdNGz56k8w80EonAvnPw+Z/et9YcrvAaUb7Rx5+uBu3Pv+cl6bu6UugDvqPBPALXsbJv2kwTMONUhAHCk0Fjo8BVfbvOQa5m/ez7zN+/ndWQOxHSJ7ZTJwdtwuJ++WWNBJiaiOmAUHTZRQ1uDBRoxk4IQQraCU4icn9yUvNY5fvr2E8x+fzfPXjMbtimv4wMBl/QCussbHH/67gle+38Lg3GQeunhY4yfJhTgMCeCa639/N2dWxl5fF6yl5Ee821pxpYcPl+7g7QWFzN20H6VgfK80bjm+gNMGdyMhtvEf3ZThbu77aBWv/LCFP583uMntf7FyFz97cwnl1V7+cO4gLh+bV+/sz4kDsvjojknc8eoi7pyxmNnr9vJ/5xx1cIYrJg4umQEzLof37zCNPcZMq727Pc4oXTWhB7kpTm57dSHnPTqb6deMpk9WYus2arWbOXo9joa3bzAjGU6/HwaeXfcYv9cUFTfCGWPlvOG5vDZ3K/9XUUNKXAy48sE9pvEATksJpSDQhbIDAzitTflk9mDIHcFT/55PSpydi0Z3P+TTynVdCaVfKzwJbmIObGmnnQ4KZN6aWAPnwYZDAjghRAScP9JNToqDG1+az7mPzubZq0YxtF5n7MB7eWxdcLZiewm3v7aQdbvLuOGYXvx0cj9ipDOuaCH5zWkOvx/Kdpp1UZP/aBpTuHpELHjz+Px8sXIXt7y8gNF/+pxfvr2UfeU1/PzUfsz6xQm8Mm0cF4zq3mTwBmbo85lDcvjPwm2UVR/czKSyxsc97y7juhfmkZXk4L+3H80V4/IbDRy6JTt5ZdpYbjuhgDcXFHL2I7NYtbPk4Be1O+Dil6HfGfDhXTDn0VZ9H1ripIFZvH7DeGp8fqY8PpvZ6/dEZsMFJ5mSytyR8J+bTaYxQPk8KN14Bg5MGWWN1887C0Oatg6aYppDFK2u91jtDxx8SgB3RNMdPUdg+0JTXTDyajbsKefTFbu4Ylz+YUuTy3GQECihBDMLrt1LKAN0I98/5fPgxYpNSiiFEBEyoXc6b988AYfdwkVPzeGT0AZwThd0HwtnPIDfr3l25kbOfXQWJZUe/n3dWH55+gAJ3kSryG9Pc5QXmaxLo23gW0ZrzdLCYu59bznj/vwF170wjzkb9nLpmDzeu3Uin//kWG4+roCcFGfY27xsXB7lNT7+s6j+tIcV20s465GZvPTdZn50dE/evWUCBZmHzlTZrBZ+Orkf/75uLAcqPJzzyCxe/WHLwR2YbLFmrMDAc+CTX9Gv6OOw9zdSBruTeefmCWQnObjquR94a37h4Z8UjqRucOV/4LhfwaoPam9Wfs8hD7UH5iQx1J3Maz9srft+DTwXUCYLJ8RBVMdm4OZPr21e8szMjditFq4c3+OwT6sroTRlRHvt2aaJSTsKftu0v5Hvn98jJZRCiIgryEzknZsn0i87iRv/PZ9nvt1g3u9tMXDdpxS5J3PN9Ln84YMVHNM3g49/fAxH92m/8Soiesm7WXOUbjeXid1avantByp57Ot1TH7wG856ZCavfL+Fsb1SeebKUXz/qxO59+yjGOJOaVFJ3fDuKfTPTuSV702g5fdrnvl2A+c+OoviSg8vXjuG35w5kFhb092RGppYkM5Hd0xiTM9Ufvn2Um57dSGlVQ3a6FvtMPU5GHQ+rqqOOfvudsXx5k0TGN0jlZ++sZiHPl8bmblaFisc9wu46n1W63yqtR2L9hy2WcvFY/JYvauUhVsPmBuSupmyzOVvNzhQD86BkwzBkczfkU1Mqkth6ZswaApFHgdvzi9k6gi3mWV4GMESyuxkBxYF20k3sy0rD7T9fgfUjRE4+PunfB48WKULpRAi4jISY3lt2jhOPSqbP/53Jb97bzlen5856/dy2kPf8N2Gvfzh3EE8feVIUuNjOnp3RZSQNXDNUbLDXCa1LIArq/by8bKdvLOwkNnr96I1jMp38efzBnPG4G4Rax+rlOKysXnc85/lfLFyNy/M2cS3a/dw0oAs/jp1MGkJhz8ga0xGYiwvXDOGJ75ZzwOfrmFJYTGPXDqcIe6UugdZbTDlKZYVeUjZOYvcDohHkp12pl8zhl++vZQHP1/Dln0V/GXK4MiUK/Q4min6b/xD/52h/gOHLXY7a2gOf/hgBa/9sIUReS5z46Ap8MGdsGuZWWtExyZdRCeiOrCEcumb4CmHkdfw4pxNeHx+pk3qGdZTy3GQ5LATY7OQneRgkzeVY8GUUTpT2nKvawW/a42dsFF+Dx5twy4lS0KINuCMsfLopSO47+NVPPXNBgr3V7JxTznOGCuvTBtH39auyxeiAQngmqM2Axd+CaXPr5m9fg9vL9jGx8t2UunxkZcaxx0n9uG84bnkp8W3ya6eMzyXP3+4ih+9OA+H3cIfzx3EZQ0albSExaK4+bgCxvRI5fZXFzL18dn88rQBXDOxR922LVY+K/gND29eTRPjsNtcjM3C/RcMIS81jgc/X8OO4koev7yJdr/N5NfgwYrVbzJwTa2BA0iItXHWkBzeW7yde84cSKLDDgPOgf/eZVq1ZzdsNCMHmEc2FZmMcUvMnw5Zg6jIGMpL333F5IFZYXdHK9dO4mNNRt/MggucrDiwtZHf8bahar9vjWTgAiWUMkZACNFWLBbFr04fQF5qHL95dxkAV43Pl+BNtAkJ4JqjZIdpCRvGiIBVO0t4Z8E23l20jV0l1SQ5bJw3Ipcpw3MZme9q826DSQ470yb1ZM6GvfxlyuDDrnVrrlE9Uvnwjknc9cYSfv/BCmav38v9Fwwx3RYD/B0cjCiluOOkPrhdTu4+VLvfZvJrjUfZUNobVubs4jHdmTFvK+8t3s5lY/MhPg16H28CuBN/F8i6+AP73KpdE12c1WKhqubg5kNtbvtC2LEITr+f1+cVcqDCw/XH9A776TXWuNoxA26XkyXBWXDt2ImyLgN38H0mgIvFbpETJEKItnX5uHxyXU7ueXcZx/WL3EgpIUJJANccpTsgIavJwYy7S6t4b9F23l6wjRU7SrBZFMf1y+R3Z+VyQv9MHPbw15xFwk8m92vT7afExfD0lSN5ftYm/vLRSk5/6Fv+dclwRvVIbdPXba6pI910S3Zww7/nc95jpt1vvbLPZtKAVwczcBwyAwcwLLAm8bUftpoADsyQ5Hdvgm3zwT2q7qhTIrgjmj82EVvZfvx+jcXSjr8L86eDzYn3qPN55pGFjMp3MTLfFfbT/fa6kyJmFlwMOt6JasdOlHVr4BorofTiVbb2/Z4KIY5Yx/fLZOYvTujo3RBRTE5HNkfJ9oPWv1V5fLy3eDtXP/8D4//yJX/870psVsW9Zw3k+1+dyDNXjeL0wd3aPXhrL0oprj26J2/dNAG7zcJFT33Ho1+tqy0D6yxzzSYUpPP2TROIsVq46Mnv+HzFrhZvS2uNBxsW7SWc9UpKKS4e3Z2l24pZtq3Y3Nj/DLDGmCxc6GNlkPcRrTq1P33ZzLb9Fe33ot6a2uYlH66rpHB/JTccG372DcBvrysFD86C8ybmdkgGDu076D6L34NfzlcKIYSIEhLANUfpDkjsht+v+W7DXn7+5mJG//Fzbn91IWt2lnLDMb34/CfH8N6tR3P1xJ4tbhbSFQ1xp/DBbUdz2qBs/v7Jap6ZubGjd+kgfbISeeeWCfTJSuD6l+bx4pxNLdqODq6Bqw3gDh90nTfcTazNwmtzAwe0jmQoOBmWvwN+f926p04S8IqOEZMziFRVxuYtG9rvRfeth5oydK/jeOqb9fTOiOfE/s0r+9ExdSXatbPgnN3aNYALZt4aW0No0V68FgnghBBCRAcJ4JqjZAdrKhOZ9LevuPip7/jvkh2cMiibV6aNZeYvTuDnp/aP+FqzriTRYefhS4bzlymD8fk1nbFaKTPRwWvXj+OE/ln89j/L+eMHK/A3NjfqEPyBDJxVe8NuGJgcZ+f0wd34z8LtVATXOA2aYk4KbJkjbSgFAKk9RwBQvGlx+71oYKj8kqoslm0rYdqkXs0uNbTF1s2pDK4x3WfL7pBh3o0FcFbtQavIdPkVQgghOpqckgxXTTlUF/N5oRVHnIWHLh7GyQOziIuRb2EopRSXjMljVL6LFTtKOnp3GhUXY+PJK0byhw9W8MzMjRTur+TBi4bhjAmvzNWvwYsNqzZz8MINvS4e3Z13Fm7jw6U7OX+kG/qdZoYmL3sLEsYDnafkVHSMhLwhAPh3LGu/Fy1aDSgeWaJIT4jl3OG5zd6EM6REvHYWnMqgd8Ve87czpm267Yaqm8nYSAbO78Wn5G+1EEKI6CAZuHAFZsCtq0rk7KG5nDMsV4K3Q+iTlcg5w5p/INherBbFvWcfxT1nDuSTFTu55Onv2FNWfdjnBc/u12DFRvO6BY7pmUqv9Hhe+yFQVhYTD31PhRXvgt9zyOeKI0RcKvus6cQXr26/19yzmprE7ny2rpRrJvZo0Xrd0OfE2CxkJTnY7A00MzrQzlm4RjNwXvwSwAkhhIgSEsCFKzADbodOJS/NeZgHi67iuqN78vhlI1m1s4TzHpvFut1lh3x88NjQq21Y0Fi077BdKIOUUlw0ujvzNu9n7a5Sc+OgqVCxl5Rdc4IPaumXIqLE3vg+ZFdtaHZpb4sVrWGdP4e4GCuXB7ukNlNcg+y12+VkTVWgi2U7lVHWlk7qg+fAWbQXv0VKKIUQQkQHCeDCFcjA7dIuurdyjpjoXE4dlM2r08ZRUe1j6uOz+X7D3iYf6w8cJHoC1cfBMspwTR3pxm5VvDY3cFBbcBLEJpGx+UNASigFeNL705tCtu1thxJkvw+9Zw2zStK5cFR3kuNaFuQ47fXfStyuOBaXJZsr7dnIhMbXwNm0By0BnBBCiCghAVy4Ahm4nTqVvFQJ4KLN8DwX79w8kbSEGK549gf+s2hbo48LHhp6MBkHq/aEnYEDSE+I5eSBWby9oJBqrw/sDuh/BjFVewKPkADuSOfIHUKs8rJtQzusgzuwGeWrZo0/h3G90lq8Gaf94Azc8lKnCZraK4DTTcyB0xorPvzShVIIIUSUkAAuXCU7qLLG47PFkZF45IwHOJLkpcXx9k0TGJ6Xwh2vLeKRL9cedDa/sQxcc0Oui0fnsb/CwyfLA7PoBk0NuVcCuCNdRoHpRFm6uR06UQY6UK7z59InK6HFm2nYAMjtcuL1K3wJOe3eifKgDJwv0GxIMnBCCCGihARw4SrZxj5LGnmpcVLmFsVS4mJ48boxnDssh/s/XcMv3lqCx1e3piZ4bFgXwHnD7kIZdHRBOm6Xs66ZSa/j8MSkmM/ld+uIl5g7EC9W1K52yMAFArhNFjf5ragsaKyEEqDcmdOOTUyamAMXaBAka+CEEEJECwngwlW6g506le5SPhn1Ym1WHrxoGLedUMDr8wq5dvpcSqoCZ/FrA7i6EsrmslgUF43qzuz1e9m8txysdnZ3P8XcKfGbsMWw3Z5HUsnatn+tPWs4YE0jMz0Tm7XlbwcNO1cGh3nvi8lq9wycwlf/Bl+NuZQSSiGEEFFCArgw6ZIdbPEky/q3I4RSip9O7sffpg5hzvq9XPjEHLYfqKwrodSBDJzf26w1cEEXjOqORVHbzGR7wSWs9efiic+J3BchuqwDiX1w17RDJ8qiVazTuRS0onwSwGGr/1bSLdmJUrCTTDOs3nv4ER2tVrsGrsHtPjPuQ1tj2n4fhBBCiHYgAVw4/D4o28VWn0sycEeYC0d35/lrRrNtfyXnPjqLZduKAfC2IgMHZtjxCf0zeWNeIR6fnzLXUZxc83f8zpY3khDRw58xkBy1h+27drbdi2iNLlrNck82fTJbF8A1rPyNsVnITnKw2Rf4fS4ubNX2m+PgNXDBDJyUUAohhIgOEsCFo2w3SvvYpV2SgTsCTeqTwRs3jcdmUVzx3A9A/SYmLcnAAVw0Oo89ZdV8uWo3utkr6UQ0i+8+BICdaxe03YuU7kDVlLHWn0ufzMRWbaqRzv24XU5WV6WYK+1SRhlcA9dgDlxgDRxWCeCEEEJEBwngwhEyQqB7qgzxPhL1z07inVsm1mYqtLWuiUlLl60d3y+DrKTYumYmyBI4YWT1GQVAZeGStnuRolUArNOt60DZFLcrjqXtOQuuqTECgS6UkoETQggRLSSAC0dgiPdOGeJ9RMtKcvD6DeO59fgChuVnAoEBwS3sHGmzWrhgZHf+t6aIbQeqAGlCKYykzDyKScBStKLtXqRoDQAbyKVHWnwrN+Y/6Ba3y8nSsni0srRjJ0o4aBFcMICTNXBCCCGihARw4aguwavseOKyiI+VTmZHsvhYG3ed0o+sFFNyZtXeVm3votHd8Wt4Y177duoTnZxS7Ijtiau0DTtRFq2izJJEYmo3YmytfCtopIbS7XJS7bfii89u1xJKGpZQBtfASQmlEEKIKCEBXDiGXcpVOe8TlyodAoWh7OZg0KI9rVq91j01jkl90llSaJqjKCmiFAElSf3I82zC7zs4uxURe9awUeXSJyupTTYfnAVXEZfTPiWUAQfFkn5zkkVJACeEECJKSAAXpi37K+me2toyIxEtrIFyLFsrM3AAF4/Oq/1cSihFrayjSFCV7NjSNlk4XbSaFTXZEVn/1thJjOAsuP32rHYpoVS1kVvjGThlkxJKIYQQ0UECuDB4fH62H6iSDpSiVvBg0IwRaF3UdfLALNLi5eBS1JeUPxSAPevnR37j5XtRFXtY48+loJUjBABoZF5d7Sw4lQUl22rnsbW5JsYISAAnhBAiWkgAF4YdB6rw+bUEcKKW1RbMwLVsDlyoGJuFqSPdrd6OiC7d+o4AoGrb0shvfM9qINCBspUjBJpSbxac9tV28207jXeh1IEmJhYJ4IQQQkQJ6cgRhq37KwBkiLeoFTybb0G3eA5cqB8d3ZMqj69N2rmLrik52UUhWcTsWRn5jQdGCKzXOfTKaLvScLfLydrqFHPlwFZIyTvk4yOh4Rw4n6caG6BkjIAQQogoIRm4MGzZFwzgZAacMGz2yJ7Nz0xy8PtzBhFrs0Z0u6Jr2+nsTXpFG6yBK1pDtXJgd3XHYW/971xTg+jrzYJr606UwV1okIHzeU0JpSXC/2eFEEKIjiIZuDCcPqgbfTIT6JYsAZwwQsuxWtOFUohDKU/pT872OfhrKrHERPDvz57VbLa46Z2VHLltNsLtcvJxWSLE0K6dKEP5PIEATkoohRBCRAnJwIUhOc7OqB6pWC3SIlAYVjmbL9qBNfsorEqza/3iiG5XF61iuadb5Ep2G5kDB5Cb4qTSb8cXl9EOAVygdLJhCWUgA2eVAE4IIUSUkABOiBaw2mNDrklgL9pGSs/hAOzbuDByG60qQZVsZ60vhz6R6EBJ01noullwue00zJuDSyg9EsAJIYSILhLACdECNlvs4R8kRCvlFRxFpY7Bsz2CnSj3mDV163ROZEYIHEJwFtyBmOx2mQUHoBsEcH5voAulZM2FEEJECQnghGgBq72uo10kulAK0ZikOAcbLd1x7l8VuY0GRgis1W56Z0SqhNLf6M3dUhwoBbtUhsnA+Rt/XER2ofazg7tQAlhs0oVSCCFEdJAATogWsNvt+LUEbqLtFcUVkFmxPoIbXI1X2fEl5RMfG5k+Vk11oYy1WclKdLDFl2YGapfvjsjrNUbpxufA+QODvG12yZoLIYSIDhLACdECMTYrHoLt1yWQE22nKrU/Ln0Af8muyGywaDWFqhu9stu2A2WQ2+VkbY3LXGnDMsrasK3hIO9ACaVVyp6FEEJECQnghGgBm1XhCU7hkPhNtKGYnMEAFK1fEJHt6T2rWemNXAMTs9Gm73K7nCwrDwSLBzZH7jUbUHWD4Ord7g90obTFyBo4IYQQ0UECOCFawG614A1k4GQNnGhLqb1MJ8riTREYJeCpgv2bWOPPoU9mYuu3V6vpCM7timNxaZK50oadKJvMwPlq8GoLdlvrB5YLIYQQnYEEcEK0QIzVUpeBE6IN9czPZ7dOwbdrWes3tncdSvtZ68+lIFIz4A7D7XJS4nfgc7jatISyNgPXoKGK3+vBgw27Vd7uhBBCRAd5RxOiBexWS+0auENUjwnRakkOOxssPUg4sKb1Gwt0oFyncyM6QqCJOd5A3Sy4qricth3mHdiJg/bFV4MHqwRwQgghooa8ownRAnarwqMlAyfax76EAjKrNoLP27oNFa3Gj4WKhB4kOSLYVv8QEVy9WXDtMsy7YQllMAMnpc5CCCGigwRwQrSALWQNnHQxEW3NkzaAWGrw7W3lOIGi1ey0ZJOfnRqZHavVdABXOwvOkmlKKA+VrovEPjScSeerwYuVGMnACSGEiBLyjiZEC8RYLdTIGjjRThzuIQDs27CwVdvRe9awytctouWTcOgy4uAsuK3+NPCUQ8W+iL52Q6rJDJy83QkhhIgO8o4mRAvYrUq6UIp2k9l7CF5toXRzKzpR+rywZy1rfJHuQHn4pJrb5WRddWAWXHEbrYOrXQPXMAPnwaOt2G3ydieEECI6yDuaEC1gtSjpQinaTUG3NDbobrB7ecs3sn8Tyu9hrT+XPhHuQNkw69WQ2+VkWUWKudKGnSiBg9OBwQycRU60CCGEiA4SwAnRAkopfEoCONE+Eh12Ntt6kFzSik6URasAWKdzKMiIcAnlYVJw9WbBtVUnSt34IG/llxJKIYQQ0UXe0YRoIa9k4EQ7OpDYlzTPDqgubdkGAiME9sf1whUfE8E9Ozy3y8k+fxx+e3zbd6JsWELp95gxAlJCKYQQIkrIO5oQLeRTpg27rIET7cGXMdBc7lzRsg0UrWGPJZ3crIwI7lV4zCw4RVV8bhuWUAa7UDbIwMkYASGEEFFGAjghWkhKKEV7Sug+FID9G1vWiVIXrWKNPyfi69/Mxv2HvDs4C644plvbDvM2O1PvmvJ78Gobdou83QkhhIgO8o4mRAvVBXByZl+0vZwefSjRTiq2tqATpd9vRgh4c+gT4RECcOgxAlA3C263NaPtulA2sTcWvwePsmGRJiZCCCGihARwQrSQXzJwoh31yUpkte6Obc/K5j+5ZBsWTwXrdC4FER4hAFCVMeyQ98farGQmxlLoT4eqYqgqifg+NFlC6ffgV9Y2eD0hhBCiY0gAJ0QL+SyBNXBKzuyLtpfosLPV1hNX6ZrDD15rqMg0MFnXRiWU40cMPexj3K441tWkmitt0MhE1TahbJCB0168gfWqQgghRDSQAE6IFpIMnGhvJcn9cPrLoWRb854Y6EBZ5OhBWht0oFRhnMRwu5ysqEg2V9pgHVxd2FZ/PZ7F75H/q0IIIaKKBHBCtJC2yFl90c4yA50odyxr3vOKVlGiksnIyg0r2ArXW75JYT/W7XKGzIJrgwxcEyWUFr+3tmOsEEIIEQ0kgBOihfwWOasv2ldyvilVLNm0qFnP00VrWKNzKIhw+eRPPTfSo+rlsB7rdsWx05+EtjraoZFJHYuWNXBCCCGiiwRwQrSQX+bAiXbWw92Nrf4MqrYtDf9JWqOLVrG6TTpQKsLtwmpGCSiq4ttqlEAwA1e/hNKqJQMnhBAiukgAJ0QLSQmlaG99shJZpbsTs7cZnSjLi7BUHWCdzqGgDUYIhMsM84aSmG5tM8y7iSYmVu2V/6tCCCGiSlgBnFLqVKXUaqXUOqXU3Y3cn6eU+koptVAptUQpdXrg9pOVUvOVUksDlydE+gsQosNYgweFkoET7SMh1sa2mF6kVGwCb3V4Twp0oFyr3fRpgxEC4cpJcQCw25rVJl0oQyK4erdatVfKnYUQQkSVwwZwSikr8ChwGjAQuEQpNbDBw34DvK61Hg5cDDwWuH0PcJbWejBwFfBSpHZciI4mZ/VFRyhP6YcVH+xZE94TilYBsNOeR1ZSbBvu2aHF2qxkJcVSqNOgvAhqKiL8Co03MZEMnBBCiGgTTgZuDLBOa71Ba10DvAac0+AxGgi0FyMZ2A6gtV6otd4euH054FRKddwRhBARpK2yBk60P2u3QQD4d4bZiXLPGipUHImZeRHtQNkSblccG2pnwRVGduP6oE9Aa6x48UsAJ4QQIoqEE8DlAqH1LoWB20LdC1yulCoEPgRua2Q7U4EFWuuD6n6UUtcrpeYppeYVFRWFteNCdLjgQaHEb6IdpeUNoFrbKdm8OLwnFK1mg86lT1bHlU8GuV1OllcGZsFFvBNlIxk4vw8LGi0llEIIIaJIpJqYXAJM11q7gdOBl5RStdtWSh0F/BW4obEna62f0lqP0lqPysjIiNAuCdHG5Ky+6AAF2Sms1bl4t4fXidK/exWrfN3aZP3bc1eP4rKxeWE/3u1ysrS07YZ5A6jQQd5+DyDlzkIIIaJLOAHcNqB7yHV34LZQ1wGvA2it5wAOIB1AKeUG3gGu1Fqvb+0OC9Fp2GICn0gKTrQf04kyD8f+1Yd/cFUxlvJdrPXnRnwGHMAJ/bP403mDw3682xXHNn+KyYhFuBNl3SDvkBt9NeZSAjghhBBRJJwAbi7QRynVUykVg2lS8l6Dx2wBTgRQSg3ABHBFSqkU4L/A3VrrWRHbayE6g9o1cEK0n4RYGztie5FQUwTlew/94CLT6GSdbosZcM3ndjnxY6E6rlvEO1HWLYELycD5vObSGtPw4UIIIUSXddgATmvtBW4FPgFWYrpNLldK/V4pdXbgYT8FpimlFgOvAldrrXXgeQXAb5VSiwIfmW3ylQjRzpQcFIoOUpXa33yye/mhHxjoQLnNlkdOsrON9+rwamfBxWZHvIRS6UZOpdRm4GQNnBBCiOgR1rua1vpDTHOS0Nt+G/L5CmBiI8/7I/DHVu6jEJ2Ssgb/+0gJpWhf9pzBsBv8O5dj6XlM0w/cs5oa7DgyemKxdPzvaXAW3B5rFpkHFkR027qRz2rXwMnJFiGEEFEkUk1MhDjyyEGh6CA57nz26CTKtx6mE2XRajarHHplJbfPjh1G3Sy4dCjdAd6aiG07GJ6qeiWUJoCT/6tCCCGiiQRwQrSQJdDERObAifbWNyuRVf7u+Hccehacf/dqVnnbpgNlS7ldcWzwpAIaShr2w2q5RjNwgQCuLlsuhBBCdH0SwAnRQhY5qy86SEFmAqt1HnHFa8Hva/xBNRWo4i2s9bs7RQOTILfLycrKyI8SUI3NgQuugZP/q0IIIaKIBHBCtJCyBVuTSwZOtK+EWBs7Hb2x+6tg/6bGH7R3LQptOlC2wQiBlqo3Cy6inSh1g0tq18DV/V8VQgghuj4J4IRoIWWTs/qi49SkDzCf7GqiE2VghMBmS/fa7o+dQW5KHFv9qab0OJKz4Grjt5BiysAaO2WLjdzrCCGEEB1MAjghWsgaXAMnCTjRAeLdR+HTCv/OJtbBFa3ChwVregHWTtCBMsjtcuLBhicuK+KjBIy6AM4XCOAsMkZACCFEFJEATogWstglAyc6Ts/sdDbpbCoLlzT+gD2r2aay6Zntat8dOwy3y8yjK43NbpsSypAMnNcTyMDZJQMnhBAiekgAJ0QL1TUx6TzZDXHk6JOVyEqdB7tXNHq/f/dqVnpzOlUDE4CcFBPAFdkiP8wbQpqZAD5PNQAWq6yBE0IIET0kgBOihaySgRMdqE9mAqv93Ykr2wI15fXv9HlQ+zawTudQ0IlGCAA47FYyE2PZrtPNGIGmumg2lz44AxcsobTKelUhhBBRRAI4IVrIZpc5cKLjxMfa2B1XYDJOu1fVv3PfBpT2ss6f26k6UAa5XU42eFPB7zUDvSOqkQycBHBCCCGiiARwQrSQVdbViA6mMwaaT3Y1aGRStBqAjcpNfmrn6UAZ5HbFsbIysDYvQp0og6WTSvtrb/N5zBgBWa8qhBAimkgAJ0QL1ZVlSQZOdIyU3ALKtOPgTpSBAM6f1hebtfP9mTez4AKlnRFbB6cPuqW2C6XMgRNCCBFFOt87uxBdhE3O6osO1icridW6O9Xbl9a/Y89qdqoMumend8yOHYbbFccWf5q5UhzhRiYha+D8gQDOJnPghBBCRBEJ4IRoIWtM4KBQSQZOdIy+WYms9nfHWrSyfuCyexWrfDkUZHS+9W9gMnBVxOJxpEV2mDcQmokLDvKWhkNCCCGiiQRwQrSQXRojiA5WkJnASp1HTM0BKN1pbvT7Yc9a1vlzOmUDEwiZBefoFrkSykDcVm+MgNesgas92SKEEEJEAQnghGghu81KjbZKF0rRYeJjbeyLLzBXdi03l8VbsPiqWKvd9OlkIwSCgrPg9tqyIjbMuzZwC2lior2mC6VNTrYIIYSIIhLACdFCMVYLXmwdvRviSJfZoBNlsAMlufRI73wdKCFkFhwZUFxYr/yzpXRt5i2klNRnMnA26RgrhBAiikgAJ0QL2awKD9ZGet8J0X7cOTls12n4gxm4QABX7Sog1mbtwD07NLfLyUZPKniroLyo1dtTDS7BrIHza4XNLl0ohRBCRA8J4IRoIbvVggebFFCKDtUnK5FV/u54gp0oi1azT6WQndWtY3fsMNyuOFZVppgrERslQL1snvZ58GDDbpX/pUIIIaKHBHBCtFBMIICTNXCiI/XNSmCVzsO+bx34PPiLVrPGl9Np178FuV1OlpYnmSuRCOD0wSWU+GrwYCWmE87CE0IIIVpK3tWEaCG7TeHVnbdETRwZemcksMqfh0V7YM8adNFq1vhzO20HyiC3K44tvsCcugg1MgFQoU1MajNw8lYnhBAiesi7mhAtZLNYqMEmc+BEh4qPtbE/MdCJcv2XWGtKWKdzKMjs7AGck1Li8MYkRaiEsrEMXCCAs8lbnRBCiOgh72pCtJDdqvAiGTjR8RzZ/fFgg2VvA7Be59K7kw7xDgrOgitzdIvoMO96TUx8HjxYsVvkJIsQQojoIQGcEC2klMKrZA2c6Hi9slNYr3Ng+wIAKpILcNg798mF2llw9uwIlVAG58DVZeCUrwaPlhJKIYQQ0UXe1YRohXLi8CgZEiw6Vp/MRFb48wAoI560rO4dvEeH57BbyUiMZQcZpoQyArPgjJDt+L14sUoJpRBCiKgi72pCtMIf1Q38O+XGjt4NcYTrm5XAKr8J2tb6cyjISurgPQqP2+VkkzcVasqgcn+rtqUCAaCiromJ8tXIGAEhhBBRRwI4IVphuy2XIlt2R++GOMIVZCawWpsM3Bp/Ln06eQOTILcrjlVVLnMlUp0oQxJwyh9cAydvdUIIIaKHvKsJ0Qp2qwUla+BEB4uLsXEguR8+rVip8zr9CIEgt8vJsrIIzoIDVEgEp/wevNiwSBMTIYQQUUQCOCFaQZojiM4iLSuP82p+z6u+Ezp9B8ogt8vJZl+auRKxTpT1AzifskVou0IIIUTnIEefQrSC3apkDJzoFPpkJbBE9yY9JZn42K4RtLhdcewnEZ8tLgIllI10ofR78UoAJ4QQIspIACdEK0gGTnQWfTMTAbpM+SQEZ8Epyp3dWl9C2UgTE4vfgw8J4IQQQkQXOfoUohUkgBOdRd+sQADXRRqYAOQGZsHts2VFcA1cHYv24lP2iGxXCCGE6Czk1KQQrTCxIJ34mM49MFkcGfpkJTA8L4Xj+2V29K6ErXYWnMqkR/HKVm1LNVJCafF78Fvk/6cQQojoIgGcEK1w92n9O3oXhABMMPTOzRM7ejeaze1ysqkqlfGV+6G6FGITW7nFkABOMnBCCCGikNR/CSGE6DBuVxxrqgOz4FrViTK4Bq4ugLNqD35pYiKEECLKSAAnhBCiw7hdTpaVJ5srERjmXT+A8+G3SAZOCCFEdJEATgghRIdxu5xs8gZnwbW8kYnSB6+Bs2oP2iIZOCGEENFFAjghhBAdxu2KYw/J+C0xEelEWT8D5zXbFUIIIaKIBHBCCCE6jNvlRGOhwpndyhJK3eDSBHBa1sAJIYSIMhLACSGE6DC1s+Ds2a1sYhJUF8DZ8MoaOCGEEFFHAjghhBAdJjgLbqfKaGUJpQncLME1cH4/VvxglQBOCCFEdJEATgghRIdyu5xs9qVB+W7wVLVya8EAzmOuSQZOCCFElJEATgghRIdyu+JYUxWYBVdc2KJtqNrKycAnvhpzTQI4IYQQUUYCOCGEEB0qN8XJ8orALLgDm1u0jWD8poI3+EwGTkoohRBCRBsJ4IQQQnQot8vJ5uAsuBZ2oqwdHxBcAxcI4LRVxggIIYSILhLACSGE6FBul5OdpKKVtdWdKBV+80lgDZzFKmMEhBBCRBcJ4IQQQnQotysOH1YqHVmt6ERpMm91JZRmDRySgRNCCBFlJIATQgjRodwuMwtuf0xrh3mHlFL6vOa6rIETQggRZSSAE0II0aEcdivpCbHsVJktLqGsDdwCl9pXba7aJAMnhBAiukgAJ4QQosO5XU62+NKgdHtdB8nmCDQvUYFLn8eUUFokAyeEECLKSAAnhBCiw7ldTtZWp4D2Q8m2Fm8nmInzemQNnBBCiOgkAZwQQogO53bFsawixVxpVSfKYABnSigtUkIphBAiykgAJ4QQosO5XU42+1o3Cw7qMnA+b6CE0iYllEIIIaKLBHBCCCE6nNvlZIcOBHAtGCWgGowR8EkGTgghRJSSAE4IIUSHc7viqMFOZWxGi0oodYPPfJ7AIG9bbGR2UAghhOgkJIATQgjR4YKz4A7EdoPiVmTgtB8IXQMnJZRCCCGiiwRwQgghOlxwFtwuldGiEspgCi5YQukPrIGzSgmlEEKIKCMBnBBCiE7B7XKy1Z8OxdvA72/ms4Nr4Myl32tKKCWAE0IIEW0kgBNCCNEpmFlwLvB7oGxnC7cSDOACGbgYWQMnhBAiuoQVwCmlTlVKrVZKrVNK3d3I/XlKqa+UUguVUkuUUqeH3PfLwPNWK6VOieTOCyGEiB5uVxzLK5LNlWaWUaray2AAZ9bA2WQNnBBCiChz2ABOKWUFHgVOAwYClyilBjZ42G+A17XWw4GLgccCzx0YuH4UcCrwWGB7QgghRD31ZsE1uxNlgxJKnymhtNkdkdo9IYQQolMIJwM3Blintd6gta4BXgPOafAYDSQFPk8Gtgc+Pwd4TWtdrbXeCKwLbE8IIYSox+1ysk2nmyst6EQJdQGcDpZQ2mUNnBBCiOgSTgCXC4SeCi0M3BbqXuBypVQh8CFwWzOeK4QQQuB2xVGJg+oYV8s6UQIq0I2ytolJjARwQgghokukmphcAkzXWruB04GXlFJhb1spdb1Sap5Sal5RUVGEdkkIIURXkptiZsEVx2Q3u4RS1Y7yDlz6TAYuRrpQCiGEiDLhBFnbgO4h192B20JdB7wOoLWeAziA9DCfi9b6Ka31KK31qIyMjPD3XgghRNRwxlhJT4hhlzUTipu5Bq52DlyghNLnoUZbsdtk2bUQQojoEk4ANxfoo5TqqZSKwTQlea/BY7YAJwIopQZgAriiwOMuVkrFKqV6An2AHyK180IIIaJLriuOrb50k4HT+vBPqFW/iQneGjzYsNtkWo4QQojoYjvcA7TWXqXUrcAngBV4Tmu9XCn1e2Ce1vo94KfA00qpOzHvoldrrTWwXCn1OrAC8AK3aK19bfXFCCGE6NrcLifri13grYTyPZAQXlWGahDAab8HL1bsFnWopwkhhBBdzmEDOACt9YeY5iSht/025PMVwMQmnvsn4E+t2EchhBBHCLfLyfIVKebdqXhL2AFcMFdXm4HzeajBht0qGTghhBDRRd7ZhBBCdBpuV1zILLjwO1HWNTEJ8HnwSgmlEEKIKCTvbEIIITqNerPgmj3MOySQ83vwaCt2q5RQCiGEiC4SwAkhhOg0uruclBCPx5bY/E6UgCW4Fs7nMU1MLPI2J4QQIrrIO5sQQohOIzclDoDi2ObOgqs/B075a/BiwyJNTIQQQkQZCeCEEEJ0GsFZcLstmc1bA9dgDpzye/AqmQEnhBAi+kgAJ4QQolPJdcVRqNNbVEJZF8B58Sp7pHdNCCGE6HASwAkhhOhU3C4n62pSoboEKg+E+azgHDjD4vfgRzJwQgghoo8EcEIIIToVt8vJyopkc6UZZZQACr+5lAycEEKIKCUBnBBCiE6l3iy4MMsoVWMZOGVrg70TQgghOpYEcEIIIToVt8tJoc4wV5o5C05pE8hZtBefBHBCCCGikARwQgghOpXuLif7SMRrdbSghDIQwEkGTgghRJSSAE4IIUSnYmbBKUpis6E43ABO17tm1V58FlkDJ4QQIvpIACeEEKJTCc6CK7JmhV1CGZwDZwk0MbFqD36LZOCEEEJEHwnghBBCdDq5rjgK/ektKKE0LNqHX7pQCiGEiEISwAkhhOh03ClONnhSoXIf1JSH8YzA2rdAKs6mPWgpoRRCCBGFJIATQgjR6bhdTlZUppgrzexECWDFKyWUQgghopIEcEIIITodt8vJZm9gFlwYZZQqtImJ1ti0VzJwQgghopIEcEIIITodtyuObTrdXAmrE2VoAOfHig8kgBNCCBGFJIATQgjR6bhdTnaTYma5NbeEUmvsSAZOCCFEdJIATgghRKeT63KisVAamw3Fhw/gVOgVXw0A2ioBnBBCiOgjAZwQQohOJy7GRlp8DEW2rGaPEggGcFhjIr9jQgghRAeTAE4IIUSn5HY5zTq4sEooQ9bABQM46UIphBAiCkkAJ4QQolNyu+LMLLiyneCtDvt52ltlPpEMnBBCiCgkAZwQQohOye1ysio4C6648JCPDR0j4PeYDJySNXBCCCGikARwQgghOiW3y8lmX5iz4EKnCASzdTbJwAkhhIg+EsAJIYTolNyuOAp1hrly2E6UdRGc12MCOItk4IQQQkQhCeCEEEJ0Sm6Xk53ahcbSrE6U3mpZAyeEECJ6SQAnhBCiU8p1OfFioyw247CdKEPnwHk8leY2ycAJIYSIQhLACSGE6JSCs+D22MIb5h3krTEZOIusgRNCCBGFJIATQgjRadXNgjtcCWXdGjhfTWANnE0ycEIIIaKPBHBCCCE6Lbcrjk2eVCjZDj5vWM/xBZuY2GLbcteEEEKIDiEBnBBCiE7L7XKyssoF2gel25t8XOgcuLoSSsnACSGEiD4SwAkhhOi0mjULLsDvDWbgZA2cEEKI6CMBnBBCiE7L7Yoza+Dg0J0odV0Gzu+pAcAqAZwQQogoJAGcEEKITivX5WS7DmTgwuxE6fcGSijtsgZOCCFE9JEATgghRKeVm+KkmhjKY9LgwOYmHxc6B84XzMDZZQ2cEEKI6CMBnBBCiE4rPtZGanwMe21ZhyyhDG1iogNr4GySgRNCCBGFJIATQgjRqbldTraTecgSSh16JRDAyRo4IYQQ0UgCOCGEEJ2a2+VkozcVigvB72/0MfUzcKaEUjJwQgghopEEcEIIITo1tyuONZUp4KuBsl2Hf4IvUEIZIwGcEEKI6CMBnBBCiE7N7XKyyXfoTpShGTh8wQyclFAKIYSIPhLACSGE6NTcLieFOsNcCWeYd2ANnF0ycEIIIaKQBHBCCCE6tfrDvA8fwClfDV5twW6ztvGeCSGEEO1PAjghhBCdWm6KkwocVNmSwxvm7ffgwYbdIm9xQgghoo+8uwkhhOjUamfB2ZueBRe6Bs7iq8GDFbtN3uKEEEJEH3l3E0II0em5XU52kBFeCaW/xmTgrKod9kwIIYRoXxLACSGE6PTcLiebvKmmhFLrRh4RmoHz4MUqJZRCCCGikry7CSGE6PTcrjhWV7nAUwEV+w75WGsgA2exSAZOCCFE9JEATgghRKfndjnZHJwFd2DzQfcrrfFpE7BZ/DV4sbXn7gkhhBDtRgI4IYQQnZ7b5WRbcBZcE50o/YG3NKv24FUSwAkhhIhOEsAJIYTo9NyuOLbWzoI7OIBTgB+TgbNqDz7JwAkhhIhSEsAJIYTo9HJTnJQQT401/hCdKE0AZ/NLBk4IIUT0kgBOCCFEp2dmwcWyz57VRAmlrs3A2bQHvwRwQgghopQEcEIIIboEMwsus8lh3joYwFEjGTghhBBRSwI4IYQQXUJuipPNvlQoPriEUtXLwHklAyeEECJqSQAnhBCiS3C7nKypckFVsfkIYQI485Zmx4NP2TtiF4UQQog2JwGcEEKILsHtimNT7Sy4g8so60oo/ZKBE0IIEbUkgBNCCNElmFlwgVECjTQyCZZQAvgtkoETQggRnSSAE0II0SW4XXF1w7wbHSVQF8Bpi2TghBBCRKewAjil1KlKqdVKqXVKqbsbuf9BpdSiwMcapdSBkPv+ppRarpRaqZT6l1JKNXy+EEIIcTi5Lid7SMJriT0ogAttYgLglzVwQgghotRhT1EqpazAo8DJQCEwVyn1ntZ6RfAxWus7Qx5/GzA88PkEYCIwJHD3TOBY4OsI7b8QQogjREKsDVdcDPvtWWQ0UkKppYRSCCHEESCcDNwYYJ3WeoPWugZ4DTjnEI+/BHg18LkGHEAMEAvYgV0t310hhBBHMrcrjp0qo5ESSo1f1b2laQnghBBCRKlwArhcIPRUZ2HgtoMopfKBnsCXAFrrOcBXwI7Axyda65Wt2WEhhBBHLrfLyWZfWpPDvIMkgBNCCBGtIt3E5GLgTa21D0ApVQAMANyYoO8EpdSkhk9SSl2vlJqnlJpXVFQU4V0SQggRLWpnwVXsgZqK2tuVpnYOHIC2ShMTIYQQ0SmcAG4b0D3kujtwW2Mupq58EuA84DutdZnWugz4CBjf8Ela66e01qO01qMyMjLC23MhhBBHHLcrzmTg4KBRAqFr4LDGtONeCSGEEO0nnABuLtBHKdVTKRWDCdLea/ggpVR/wAXMCbl5C3CsUsqmlLJjGphICaUQQogWqTcLLqSM0nShDHlLkzECQgghotRhAzittRe4FfgEE3y9rrVerpT6vVLq7JCHXgy8prXWIbe9CawHlgKLgcVa6/cjtvdCCCGOKG5XHIXBWXDF9RuZ6Hpz4CQDJ4QQIjqFdYpSa/0h8GGD237b4Pq9jTzPB9zQiv0TQgghauW6nOzGhU/ZsIZ0olToegGcskkTEyGEENEp0k1MhBBCiDaTEGsjOS6WYntGvRJKDWgla+CEEEJEPwnghBBCdClmFlxWvSYmJgNX95amZA2cEEKIKCUBnBBCiC7F7XKyxZd60DBvf70SSsnACSGEiE4SwAkhhOhS3C4na2tc6NKd4K0BCIRuUkIphBAi+kkAJ4QQoksxs+DSUWgoKay9PbSJiUWamAghhIhSEsAJIYToUnJTDp4Fp9D1mpgoycAJIYSIUhLACSGE6FLcqU4KawO44Dq4+k1MLLIGTgghRJSSAE4IIUSXkpviZKdOMyWTxaGjBEJLKCWAE0IIEZ0kgBNCCNGlJDrsxMc5KQmZBaegQQZO1sAJIYSIThLACSGE6HLcLie7LBn1RgnokCaUkoETQggRrSSAE0II0eW4U+LY6kuHYhPANRzkbbXHdtSuCSGEEG1KAjghhBBdTu0suJLt4PMCMkZACCHEkUECOCGEEF2O2+Vksy8N5fdC6Q5Ao1XdW5pNMnBCCCGilARwQgghuhy3K65uFlygE6WUUAohhDgSSAAnhBCiyzGz4DLMlQNbQ4onDauUUAohhIhSEsAJIYTocnJTnHUZuEAnSimhFEIIcSSQAE4IIUSXk+iw44yLp8zmguItgS6UdXk4m13GCAghhIhOEsAJIYTokswsuKzaYd71MnAxjo7aLSGEEKJNSQAnhBCiS3KnxFHoT4MDJgNHvQycrIH7//buPNqysj7z+PepAaoQkFJmK1IOjRIVVFSiYgQkaqu0tkO0BQl2B2M6LRqDNg4xDMtepm2xwY4jKkbbrFYwElo7DmlHwBYkKKDIJChot6BISCRA1f31H3vfqktZF6rOa9U5+5zvZ6296kz31nue9Z67z2+/796vJGk6WcBJkgZp9aqVXH3nKurWGwjF3IICbvlyR+AkSdNp2bgbIEnSKFavWsk163YlS+5gR4B0BdxcheXbOQInSZpOjsBJkgbpbmvBAfO7tLtYxvKlGy8sIEnSdLCAkyQN0ur7rbxbAVf9CNxdLGX5EndvkqTp5B5OkjRId1sLDqgFI3BLljgCJ0maThZwkqRB2mnFcpbtcF9+uXQnYMMyAmtZOs5mSZK0VVnASZIG6wG7rOSmJbv397pRt7Xx+lySpOllASdJGqzVq1ZyQz+Ncv4cuHVeYFmSNMUs4CRJg7V61Q5cc+f9+nvzUygt4CRJ08sCTpI0WKtXreT6dfcHNpwDt84plJKkKWYBJ0karNWrdlg/hXKe58BJkqaZBZwkabC6c+B2AxyBkyTNBgs4SdJgPWDVgrXg1hdwy8fYIkmSti4LOEnSYO28Yjm1YhXXzu3Jz5bvBcCcI3CSpClmASdJGrTV99uBw+58B1/b5bkArFviCJwkaXpZwEmSBm31qpVASL8OnCNwkqRpZgEnSRq01at2AFhfwNUSCzhJ0vSygJMkDVo3AgfJUgDmlmw3zuZIkrRVWcBJkgbtV0bgnEIpSZpiFnCSpEHbMALXnwPnRUwkSVPMAk6SNGgP6As4+gIOz4GTJE0xCzhJ0qDtvGI5h++3O49cvQsAc0s9B06SNL08TClJGrwzfu/x3HrLz+CLOAInSZpqjsBJkqbD/BRKR+AkSVPMAk6SNBXm6zeWehETSdL0soCTJE2F9csIWMBJkqaYBZwkaSosWb6Sa+f25Lb7PGjcTZEkaauxgJMkTYVly5fzjHXv5KbVTx93UyRJ2mq8VJckaSpsv2wpZ73ySTx09x3H3RRJkrYaCzhJ0tQ44Dd2GXcTJEnaqpxCKUmSJEkDYQEnSZIkSQNhASdJkiRJA2EBJ0mSJEkDYQEnSZIkSQNhASdJkiRJA2EBJ0mSJEkDYQEnSZIkSQNhASdJkiRJA2EBJ0mSJEkDYQEnSZIkSQNhASdJkiRJA2EBJ0mSJEkDkaoadxvuJslNwPXjbsdWtCtw87gbMWDm18b82phfG/NrY35tzK+N+bUxvzazmN8+VbXbpp6YuAJu2iW5qKoeN+52DJX5tTG/NubXxvzamF8b82tjfm3Mr4353Z1TKCVJkiRpICzgJEmSJGkgLOC2vfePuwEDZ35tzK+N+bUxvzbm18b82phfG/NrY34LeA6cJEmSJA2EI3CSJEmSNBAWcFtBkoy7DUNmfpIkaaj8HqOtzQJuKyjnpbbafv6GfwS1rSXZbsFt+98WSrLjgtvmtwXSefC42zFkSQ5Lcp9xt2OI+v73B0n2GndbhirJW5Ps5/fA0SR5wPw+2P3HPbOA+zVKclSSryc5Ocnzx92eoUnykiRXAP81yWvBYnhzJTk2ybuTPGTcbRmqJC9LcgFd//tjsP9tiSRHJrkIeHuSk8H8tkSSpcDngA8l2eTCrVpc3/++BRwK3DXu9gxNkmcAVwBPAra7l5drI0lemuSrwL8Hjhp3e4YmyYuTXAa8E/gouP+4N8vG3YBpkeQQug/u64A54OQkVNWnkiytqnXjbN+kS7IPcBzwb4FbgLOS3FxVfznelk22JEuAFwGvB34CHJTkxqr65/G2bDiSbA+8ge6L3+uA5cBJSb5dVf97rI0bgCQr6HI7DHgt8DPgzCSfqKrLxtq4YVlK98V5CXBwknOrau2Y2zTxkiwDXgO8CfiXVfWN8bZoePoMnwUcV1Wf2+i5+EV6cUl2Bt4OrKHbj+wH3Ld/zuw2Q5LHA68GXlFV5yf5XpLHVtXF427bJHMErsHCqVZ0R63OrqrzquoC4FLgbQAWb4tbMES+A/B94PKq+h7dDvlPktxvXG0bgqqaAy4BHg+8B/htuh2INlNV3QFcBjyvqr4OfB04D9hjrA0biP5gwaer6tCq+ipdEXIVcON4WzYc/UG+O4FzgU8B/w7YfbytGoa+yL0K+BhwfZLtkrwgyd5jbtpg9BnuC/woyX2T/EmS37EAuXdV9Q/AB6rqGVV1HlDA7/bPmd3meTDwtb5424Nuf/yL8TZp8lnAjSjJnwEfT/Ly/qFLgFf1R6MBfgosTfKG/vVmvUCSNyY5aMEfuGXAbnSFHFX1BeBKupEl81tgPrv+doBrq+oXwFlAgKckWTXGJk68hRn2PltVtyRZUlV3AfsDt42peRNvoz64pKou7W8/je6L9O7AqUmOn3/N2Bo7gTbKb1lVrUuyJ3A4cDrdaPrvJnlekp3G2dZJtInP7wXA9cD/Ai4G/jXwkSRv6l9v/1tgo/63NMl96Q6gPh74a7p98RvpppPb/zaycX5VddGCp88G1ibZfzytm3yb+Pz+EHhgkk8CF9J9jzkjyZ/3r/dcuE3wj9oI+vNjnkw34vG0JKfTnbvwReADSb4N7AgcCzwmyfb9SMnMS7JXkrPpCrOPzT/efwH8J+APFrz8BOAlSXYxv01nV527+iOld9HtPA4EHrvRz/oHkMX7H3A7dCOaSVYCa+kOymiBRfrg3IL+9SPgKVV1ON0MhBOT7Ornt7NIfmv7AuNm4MI+qx/R5fcfAGdw9O5h//FTulHzzwLPrKqjgD8Gjk9yf/tfZ5H+t66qbqU7YHUk8JmqOoHuPK4n0o2OiMXz2+hlq4Af4PfrX3EPn98LgGOA7wFvrqoX0s1CODrJAxzJ3DQ72BZKd6L5Y4CTqurvgJOBO4E3VNXvA/8ROLaq3kx3FOH6qrrDL9Dr3Qp8sqp2AX6R/mIlvT8DnpfkQICquoauKN7xV37LbNpkdv0R/AKoqs8D1wGPSvLsJH/UP+4fwM5i/W/h38KdgR2r6oYkByR56bZu5ARbLL9lAFV1ZVX9vL/9fbopgU4F3GCT+fUFxu7AkUm+AjwT+Bvgm4Dns25wT/uPC+n2yzcA9Odf/i2w6zZv5eS6p/zeS3ewYLskO1TVjXSzYB607Zs5sRbdB8+/oKp+AOwDPLp/zu/ZG9xTfgXsBFwO63M8n25qrzYhfq9b3Mbzv+fvJ3k7sGtVvbz/cB4I/ClwSlVd2L92CfBB4Kqq+k/jaP+4LTZ/PslOVXVbkoOBc4C9+vM/6D/Qj6SbzrEGeDjwrKq6fdu1fPy2NLv5nUQ/GvLE/rm1wNur6p3btPETYoQMl/ZT2Z5Fd0GELwBHAKdX1Ue2bevHb5Q+OD/S0e+QTwUeAryoqn65TRs/AUb8+/c24Kaqekc/Enw28IdVdf02bfwEaOx/y+n634OBF9QMXtRpxP73YrpplHcCK+jOqX5+Vf1wGzZ9Ioy4D06/DzkOOKiqjtzW7Z4UI/a/U+mK3/cBTwcOBo6oqpu2YdMHwyMD92zp/I2NOuP7gdVJDux3GNfRHSl9VP/aw+iOHMzR7URm1d3ym7/df3hT3QUjvkJ35G/eaf22N/CPwHNmrXjrbVF2VTXXF2+70V0R61zgobNavPW2NMP5qTD7AQfQrUf4lFks3npb3Af71x5F9/dwHTNavPU2N7/3LXjuhKp6R3/7duBfzWLx1hu1/z2X7py4+f43c8Vbb5T97yeA/0J3AYlbgafOYvHWG2UfPL8PuQP46xmfeTVK/3sD8FXgD/v7T7N4W5wjcJuQ5Nl08+e/C5xV3ZXV5qeqrU139cnXAAdW1Yv7504HvlNVZyR5ILCun4Iwc+4hv6XAXD+KOZ/lHnTznvelv+pfVV2eGV16oSG73fvnr0h3ztHN43oP49bY/34O7An8U1VdOZ53MF6N+f0D3YHBJf0UmJnTkN9udLldvtjR61nQ2P9uoztwuqyqrhvLGxizxn3Ikqq6zP438ud3WVVdmmR5deekz5yG/PYE7qyqK5OsnNED91vEEbiNJFkDvBV4F13HekWS34f1l9qFbo2PjwL3T/KmdIsnP4x+8dCq+uEMF29rWDy/df2Hdzc2nDPz/+gum/1T4MPzv2dGi7c1jJ7dmfRHvGa8eFtDW4arqurvZ7h4W0NbfjtV1fUzXLytYfT81o/0zvCX5zW09b8dq+qGGS7e1tCW3/y51Pa/0T6/c/3js1q8rWH0/D5Etwbr/OwD3ZuqcluwAU8D/lt/ewVwCPBt4H79Y++mm7e7J/AIus56EfCWcbd9ErbNyO8vgE8Dv0l3AOFldFNQXzfuto97MzszHPdmfuZnfsPdzM/8zG92trE3YNwb8EK6k03n7z+Mbg2eFQseew/dJZ33pjvKsmqj37H9uN/HUPMDHgfsMu73YXbD3MzQ/MxvuJv5mZ/5DXczvzHnP+4GjO2Nd/O9vwL8mO6IwJIFz/0l8M7+duguB3s2/VGE/vGl434PA89v2bjfg9kNdzND8zO/4W7mZ37mN9zN/CZjm9lz4Kpb+PMcuvV2fsLdF5A+CTgiySOq6223012R6c50ltQMnqO10K8hv7W/8ktnhNm1M8M25tfG/NqYXxvza2N+bcxvMsxkAZcNCyu+i+5KOZ8Hnp1kL1i/gPQHgXenW6viKLorXK2rztwYmj0xzG90ZtfODNuYXxvza2N+bcyvjfm1Mb/JMRPLCGTDAr2LLSy4B/A6YG1VnbDg8aOBQ4H7AMdV1f/dZo2eIOY3OrNrZ4ZtzK+N+bUxvzbm18b82pjf5JrqAi7Jk4FXANcC76qqn/ePr1+PYsH9J9Ot7XYccCdwV1XdkmS76leJnzXmNzqza2eGbcyvjfm1Mb825tfG/NqY3+Sb2imUSR5Md8n/LwH7AKckeRbcbT2K7ZNs39//KnA5cBndyZm79q+dyc5nfqMzu3Zm2Mb82phfG/NrY35tzK+N+Q3D1BZwwBOA71XVmcDxwCV0J1buBZDkZOAMYP7+K+lOxHwfsH9VXTWGNk8S8xud2bUzwzbm18b82phfG/NrY35tzG8Alo27Ab8uSY6gO1JwUVV9A/gm8KokD6yqHyY5D3gI8JIkFwIPpVt8+7r+V1wNPKmqrh5D88fO/EZndu3MsI35tTG/NubXxvzamF8b8xumwY/AJdkrybnA64FVwIeTPKOqrgUuAF7Uv/T7dEO8OwOXVtVLq+rqfv4uVfXFWex85jc6s2tnhm3Mr435tTG/NubXxvzamN+wDb6Ao1vJ/WtV9ZSqOgU4je7ES4CvAY9KclB167bdCPx2Vd0K3eVQa8bXc8P8WphdOzNsY35tzK+N+bUxvzbm18b8BmyQBVySo5MckmR74O+Ajy54+mfAlf3t/wP8PXBqkh2BRwDXJ9kBoGZ0PQrzG53ZtTPDNubXxvzamF8b82tjfm3Mb3oM5hy4JAH2BD4OzAHXAMcCr66qnyRZXlV30Z1UuQqgunUnTkuyD/Ahujm+R1fVL8fxHsbJ/EZndu3MsI35tTG/NubXxvzamF8b85tSVTXxG7C0/3df4GPzj9GtBP+pjV5zLnB4f3v3/t9lwE7jfh/mN7zN7Mxw3Jv5mZ/5DXczP/MzP7etsU30CFy6EyRPAZYm+SzdCZTroFuLIsmrgR8neWpVfSXJdsBNwJVJ3go8J8khVXULcNuY3sbYmN/ozK6dGbYxvzbm18b82phfG/NrY37Tb2LPgUvyVOBbdMO5V9N1xLuAQ5M8AdbPwT0ROKn/sRXAMXTzeneiO5JwyzZt+IQwv9GZXTszbGN+bcyvjfm1Mb825tfG/GbDJI/AzQHvqKqPAiR5DPAg4C3Ae4ADkywBPg0clmQ1sDfwMeDUqrpkHI2eIOY3OrNrZ4ZtzK+N+bUxvzbm18b82pjfDJjYETi6owef6IeBAc4DHljdyvBLk7yqP4KwGpirqhuq6ptVdbSdDzC/FmbXzgzbmF8b82tjfm3Mr435tTG/GTCxBVxV/bKq7qgN60z8Dt38XICXA/sl+Z/AX9F11vkr7Qjza2F27cywjfm1Mb825tfG/NqYXxvzmw2TPIUSWH8iZgF7AH/TP3wb8EbgkcAPqupGgKrukjnawPxGZ3btzLCN+bUxvzbm18b82phfG/ObbhM7ArfAHLAcuBnYvz9q8Kd0w75fn+98WpT5jc7s2plhG/NrY35tzK+N+bUxvzbmN8UyhKI7yW8B5/fbh6vqg2Nu0qCY3+jMrp0ZtjG/NubXxvzamF8b82tjftNrKAXcauBldFfHuWPc7Rka8xud2bUzwzbm18b82phfG/NrY35tzG96DaKAkyRJkiQN4xw4SZIkSRIWcJIkSZI0GBZwkiRJkjQQFnCSJEmSNBAWcJIkSZI0EBZwkqSJkeRNSS5P8p0klyQ56F5ef2KS4xv+v1cmObq/fUySvRc895okO2zh7zukXzB3U88dnOSbSa7ot1eM2m5J0uxaNu4GSJIEkOSJwHOAx1bVHUl2Bbbbmv9nVb13wd1jgMuAH/f3XwN8DPhl6/+TZE/g48Dzquri/r19LsmNVfWZ1t8vSZodjsBJkibFXsDN8wvOVtXNVfVjgCTX9UUPSR6X5MsLfu6AJBckuSrJsf1rDknylSTnJLk2yduSHNmPgF2a5CH9605McnySFwKPA/57P/L3amBv4EtJvtS/9un9/3Nxkk8m2bF//Jn9iNrFwPMXeW9/BJxZVRfPvzfg9cAJ/e84M8npSc7v2/vCX1eokqTpYgEnSZoUnwd+I8mVSd6d5Kmb+XP7A4cBTwTesmAa5AHAK4H9gJcB+1bVE4AzgFct/AVVdRZwEXBkVT26qk6jG4k7tKoO7YvHNwOHV9Vj+9e+NskK4APAEcCBwJ6LtPERwLc2euyi/vF5ewEH041Cvm0z37skacZYwEmSJkJV/SNdEfQK4CbgfyQ5ZjN+9Jyqur0f1foS8IT+8Qur6if9iN41dAUiwKXAmi1s3m8Bvwmcl+QS4PeAfYCHAz+oqquqquimXI7q01U1V1XfBfZo+D2SpCnmOXCSpIlRVeuALwNfTnIpXaF0JrCWDQcdV2z8Y4vcv2PBY3ML7s+x5fu/AF+oqn9ztweTR2/mz3+Xrjg9Z8FjBwKXL7i/sL3ZwvZJkmaEI3CSpImQ5GFJ/sWChx4NXN/fvo6u4AF4wUY/+twkK5LcHzgEuHDEJtwG7LTI/W8AT07y0L6t90myL3AFsGb+nDrgbgXeAn8BHDNf8PVt/XPgP4/YVknSjLKAkyRNih2BjyT5bpLv0E1ZPLF/7iTgtCQXAes2+rnv0E2d/AZwyvyFT0ZwJvDe/iImK4H3A3+b5EtVdRPdVSr/qm/bBcDDq+qf6aZ8fqa/iMlPN/WLq+onwFHAB5JcAZwPfKiqzh2xrZKkGZVuyr4kSZIkadI5AidJkiRJA2EBJ0mSJEkDYQEnSZIkSQNhASdJkiRJA2EBJ0mSJEkDYQEnSZIkSQNhASdJkiRJA2EBJ0mSJEkD8f8BI/KeDF7AgG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#set date as index\n",
    "new_data.set_index('Submitted On',inplace=True)\n",
    "\n",
    "#plot data\n",
    "fig, ax = plt.subplots(figsize=(15,7))\n",
    "new_data.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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